Overview

Brought to you by YData

Dataset statistics

Number of variables120
Number of observations23310
Missing cells161831
Missing cells (%)5.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.0 MiB
Average record size in memory1.8 KiB

Variable types

Numeric20
Text8
Categorical86
DateTime4
Unsupported2

Alerts

IDGamme has constant value "0" Constant
IDClient has constant value "0" Constant
TempsClient has constant value "0.0" Constant
IdProcess has constant value "0" Constant
prixMP has constant value "0.0" Constant
Valeur has constant value "0.0" Constant
Cadence has constant value "0.0" Constant
IdArticleBase has constant value "0" Constant
SemiFini has constant value "0" Constant
ValeurTissu has constant value "0.0" Constant
ValeurFourniture has constant value "0.0" Constant
ValeurMP has constant value "0.0" Constant
TypeTarif has constant value "0" Constant
IdMeilleurOF has constant value "0" Constant
BaseStylisme has constant value "" Constant
IDTypeMatiereBase has constant value "0" Constant
IDVarianteModele has constant value "0" Constant
IDGenre has constant value "0" Constant
IDBroderie has constant value "0" Constant
IDSerigraphie has constant value "0" Constant
IDGarniture has constant value "0" Constant
IDTypeAccessoire has constant value "0" Constant
IDTransfert has constant value "0" Constant
IDCouleurGarniture has constant value "0" Constant
IDCouleurBroderie has constant value "0" Constant
IDCouleurSerigraphie has constant value "0" Constant
PrixEmballage has constant value "0.0" Constant
StockMin has constant value "0" Constant
StockAlerte has constant value "0" Constant
ValeurMPEuro has constant value "0.0" Constant
ValeurMPAutre has constant value "0.0" Constant
ValeurMPTunisie has constant value "0.0" Constant
ValeurMPEuromed has constant value "0.0" Constant
AQL has constant value "0.0" Constant
AQLMineur has constant value "0.0" Constant
IDNiveauControle has constant value "0" Constant
AQLCritique has constant value "0.0" Constant
IDCategorie has constant value "0" Constant
IDCategoriereclamation has constant value "0" Constant
IDCartouche has constant value "0" Constant
IDArticleParent has constant value "0" Constant
isParent has constant value "0" Constant
QteFils has constant value "0" Constant
TempsAtelier has constant value "0.0" Constant
TempsFinitions has constant value "0.0" Constant
IDTypeMatelassage has constant value "0" Constant
IDMP has constant value "0" Constant
IsMP has constant value "0" Constant
IDDecorArticle has constant value "0" Constant
IsSemiFini has constant value "0" Constant
TempsUnitaire has constant value "0.0" Constant
TauxSondageQlte has constant value "0.0" Constant
IDNorme has constant value "0" Constant
DDV has constant value "" Constant
FraisTransport has constant value "0.0" Constant
AutresFrais has constant value "0.0" Constant
IDArticleEtqEntretien has constant value "0" Constant
Ecologique has constant value "0" Constant
TauxDefectueux has constant value "0.0" Constant
Publier has constant value "0" Constant
Ordre has constant value "0" Constant
TauxCommissionCA has constant value "0.0" Constant
CODE_OLD has constant value "" Constant
PrixEtude has constant value "0.0" Constant
AppliqueFodec is highly overall correlated with IDAr_Collection and 13 other fieldsHigh correlation
Boutonnage is highly overall correlated with MatiereHigh correlation
CodeDouane is highly overall correlated with IDArFamille and 1 other fieldsHigh correlation
Emballage is highly overall correlated with MatiereHigh correlation
Etat is highly overall correlated with ModifiePar and 1 other fieldsHigh correlation
IDArFamille is highly overall correlated with CodeDouane and 1 other fieldsHigh correlation
IDArSousFamille is highly overall correlated with MatiereHigh correlation
IDAr_Collection is highly overall correlated with AppliqueFodec and 14 other fieldsHigh correlation
IDAr_Couleur is highly overall correlated with IDAr_Collection and 6 other fieldsHigh correlation
IDAr_Looks is highly overall correlated with IDSupportArticle and 1 other fieldsHigh correlation
IDAr_Theme is highly overall correlated with Matiere and 1 other fieldsHigh correlation
IDArticle is highly overall correlated with AppliqueFodec and 13 other fieldsHigh correlation
IDFibreComposition is highly overall correlated with MatiereHigh correlation
IDFournisseur is highly overall correlated with AppliqueFodec and 12 other fieldsHigh correlation
IDGrille is highly overall correlated with MatiereHigh correlation
IDPatronnage is highly overall correlated with AppliqueFodec and 13 other fieldsHigh correlation
IDPays is highly overall correlated with AppliqueFodec and 3 other fieldsHigh correlation
IDPlanComptable is highly overall correlated with IDAr_Collection and 7 other fieldsHigh correlation
IDSaison is highly overall correlated with AppliqueFodec and 7 other fieldsHigh correlation
IDSupportArticle is highly overall correlated with IDAr_Looks and 1 other fieldsHigh correlation
IDUsine is highly overall correlated with AppliqueFodec and 13 other fieldsHigh correlation
IDcomplexite is highly overall correlated with AppliqueFodec and 13 other fieldsHigh correlation
Matiere is highly overall correlated with AppliqueFodec and 36 other fieldsHigh correlation
ModifiePar is highly overall correlated with AppliqueFodec and 8 other fieldsHigh correlation
NbrColisPalette is highly overall correlated with Matiere and 1 other fieldsHigh correlation
NbrPiecesColis is highly overall correlated with IDAr_Theme and 2 other fieldsHigh correlation
NomenclatureValide is highly overall correlated with Matiere and 1 other fieldsHigh correlation
NomenclatureValidePar is highly overall correlated with Matiere and 1 other fieldsHigh correlation
NumInterne is highly overall correlated with AppliqueFodec and 11 other fieldsHigh correlation
PieceCarton is highly overall correlated with AppliqueFodec and 10 other fieldsHigh correlation
PoidsBrut is highly overall correlated with MatiereHigh correlation
PoidsEmballage is highly overall correlated with IDAr_Collection and 4 other fieldsHigh correlation
PoidsNet is highly overall correlated with IDAr_Collection and 7 other fieldsHigh correlation
Prix is highly overall correlated with IDArticle and 4 other fieldsHigh correlation
PrixAchat is highly overall correlated with Matiere and 2 other fieldsHigh correlation
PrixFac is highly overall correlated with Matiere and 2 other fieldsHigh correlation
PrixOutlet is highly overall correlated with MatiereHigh correlation
ReseauArt is highly overall correlated with MatiereHigh correlation
SaisiPar is highly overall correlated with AppliqueFodec and 9 other fieldsHigh correlation
SupportArt is highly overall correlated with MatiereHigh correlation
TauxTVA is highly overall correlated with AppliqueFodec and 5 other fieldsHigh correlation
TypeArticleService is highly overall correlated with IDAr_Collection and 3 other fieldsHigh correlation
SaisiPar is highly imbalanced (61.8%) Imbalance
ModifiePar is highly imbalanced (61.9%) Imbalance
Etat is highly imbalanced (94.2%) Imbalance
TauxTVA is highly imbalanced (59.9%) Imbalance
CodeDouane is highly imbalanced (99.4%) Imbalance
NomenclatureValide is highly imbalanced (99.8%) Imbalance
TypeArticleService is highly imbalanced (75.4%) Imbalance
NomenclatureValidePar is highly imbalanced (99.9%) Imbalance
NbrPiecesColis is highly imbalanced (99.8%) Imbalance
NbrColisPalette is highly imbalanced (99.9%) Imbalance
PoidsEmballage is highly imbalanced (97.9%) Imbalance
Emballage is highly imbalanced (99.9%) Imbalance
Boutonnage is highly imbalanced (99.9%) Imbalance
SupportArt is highly imbalanced (99.9%) Imbalance
ReseauArt is highly imbalanced (99.9%) Imbalance
IDFibreComposition is highly imbalanced (99.2%) Imbalance
PrixOutlet is highly imbalanced (99.4%) Imbalance
ModifieLe has 16304 (69.9%) missing values Missing
Image has 23310 (100.0%) missing values Missing
Observations has 6938 (29.8%) missing values Missing
DateValidationNomenclature has 23307 (> 99.9%) missing values Missing
Dimensions has 23310 (100.0%) missing values Missing
ArticleLong has 4892 (21.0%) missing values Missing
DateMEP has 18629 (79.9%) missing values Missing
CompositionMatiere has 21743 (93.3%) missing values Missing
Matiere has 23244 (99.7%) missing values Missing
Prix is highly skewed (γ1 = 101.6577525) Skewed
PrixFac is highly skewed (γ1 = 101.6577602) Skewed
PoidsBrut is highly skewed (γ1 = 149.1450677) Skewed
PoidsNet is highly skewed (γ1 = 114.1755232) Skewed
PrixAchat is highly skewed (γ1 = 101.6577726) Skewed
IDArticle has unique values Unique
Image is an unsupported type, check if it needs cleaning or further analysis Unsupported
Dimensions is an unsupported type, check if it needs cleaning or further analysis Unsupported
IDAr_Collection has 15990 (68.6%) zeros Zeros
IDAr_Couleur has 7245 (31.1%) zeros Zeros
Prix has 4974 (21.3%) zeros Zeros
PrixFac has 3136 (13.5%) zeros Zeros
PoidsBrut has 23241 (99.7%) zeros Zeros
PoidsNet has 8493 (36.4%) zeros Zeros
IDArSousFamille has 464 (2.0%) zeros Zeros
IDGrille has 430 (1.8%) zeros Zeros
IDSaison has 767 (3.3%) zeros Zeros
NumInterne has 15121 (64.9%) zeros Zeros
IDFournisseur has 15832 (67.9%) zeros Zeros
IDAr_Theme has 21299 (91.4%) zeros Zeros
PrixAchat has 2297 (9.9%) zeros Zeros
IDPays has 431 (1.8%) zeros Zeros
IDUsine has 16505 (70.8%) zeros Zeros
IDSupportArticle has 22366 (96.0%) zeros Zeros
IDAr_Looks has 22537 (96.7%) zeros Zeros
IDPlanComptable has 18078 (77.6%) zeros Zeros

Reproduction

Analysis started2025-03-09 14:50:06.744932
Analysis finished2025-03-09 14:51:45.707253
Duration1 minute and 38.96 seconds
Software versionydata-profiling vv4.13.0
Download configurationconfig.json

Variables

IDAr_Collection
Real number (ℝ)

High correlation  Zeros 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4002145
Minimum0
Maximum13
Zeros15990
Zeros (%)68.6%
Negative0
Negative (%)0.0%
Memory size182.2 KiB
2025-03-09T15:51:45.746342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.1957927
Coefficient of variation (CV)1.7480907
Kurtosis0.85052979
Mean2.4002145
Median Absolute Deviation (MAD)0
Skewness1.5444117
Sum55949
Variance17.604676
MonotonicityNot monotonic
2025-03-09T15:51:45.822013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 15990
68.6%
5 1672
 
7.2%
4 1238
 
5.3%
12 1110
 
4.8%
13 878
 
3.8%
11 828
 
3.6%
10 581
 
2.5%
1 542
 
2.3%
2 160
 
0.7%
6 137
 
0.6%
Other values (4) 174
 
0.7%
ValueCountFrequency (%)
0 15990
68.6%
1 542
 
2.3%
2 160
 
0.7%
3 23
 
0.1%
4 1238
 
5.3%
5 1672
 
7.2%
6 137
 
0.6%
7 63
 
0.3%
8 1
 
< 0.1%
9 87
 
0.4%
ValueCountFrequency (%)
13 878
3.8%
12 1110
4.8%
11 828
3.6%
10 581
 
2.5%
9 87
 
0.4%
8 1
 
< 0.1%
7 63
 
0.3%
6 137
 
0.6%
5 1672
7.2%
4 1238
5.3%

IDArticle
Real number (ℝ)

High correlation  Unique 

Distinct23310
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11797.411
Minimum2
Maximum23883
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size182.2 KiB
2025-03-09T15:51:45.905555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile1197.45
Q15859.25
median11687.5
Q317739.75
95-th percentile22683.55
Maximum23883
Range23881
Interquartile range (IQR)11880.5

Descriptive statistics

Standard deviation6874.4423
Coefficient of variation (CV)0.58270769
Kurtosis-1.187902
Mean11797.411
Median Absolute Deviation (MAD)5937
Skewness0.037373817
Sum2.7499766 × 108
Variance47257957
MonotonicityStrictly increasing
2025-03-09T15:51:46.005687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23883 1
 
< 0.1%
2 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
Other values (23300) 23300
> 99.9%
ValueCountFrequency (%)
2 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
ValueCountFrequency (%)
23883 1
< 0.1%
23882 1
< 0.1%
23881 1
< 0.1%
23880 1
< 0.1%
23879 1
< 0.1%
23878 1
< 0.1%
23877 1
< 0.1%
23876 1
< 0.1%
23875 1
< 0.1%
23874 1
< 0.1%

Code
Text

Distinct20709
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-03-09T15:51:46.236297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length20
Mean length7.5146289
Min length2

Characters and Unicode

Total characters175166
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19365 ?
Unique (%)83.1%

Sample

1st rowBHNP282
2nd rowReport à Nouveau
3rd rowAENC88
4th rowAENU104A
5th rowAENU102AA
ValueCountFrequency (%)
cenc154 45
 
0.2%
c1 32
 
0.1%
t1 30
 
0.1%
r1 27
 
0.1%
cent114 20
 
0.1%
cent094 17
 
0.1%
cenc168 16
 
0.1%
p1 16
 
0.1%
cenc216 14
 
0.1%
xenj413 14
 
0.1%
Other values (20456) 23306
99.0%
2025-03-09T15:51:46.717268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 24876
 
14.2%
A 12813
 
7.3%
1 12709
 
7.3%
E 11963
 
6.8%
H 11780
 
6.7%
2 7766
 
4.4%
0 7549
 
4.3%
4 6060
 
3.5%
X 5973
 
3.4%
3 5902
 
3.4%
Other values (55) 67775
38.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 109491
62.5%
Decimal Number 64747
37.0%
Space Separator 665
 
0.4%
Lowercase Letter 237
 
0.1%
Dash Punctuation 22
 
< 0.1%
Connector Punctuation 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 24876
22.7%
A 12813
11.7%
E 11963
10.9%
H 11780
10.8%
X 5973
 
5.5%
R 5331
 
4.9%
C 5186
 
4.7%
T 4203
 
3.8%
B 3639
 
3.3%
D 3315
 
3.0%
Other values (17) 20412
18.6%
Lowercase Letter
ValueCountFrequency (%)
e 43
18.1%
a 29
12.2%
o 22
9.3%
i 18
 
7.6%
t 18
 
7.6%
u 13
 
5.5%
l 12
 
5.1%
p 12
 
5.1%
n 10
 
4.2%
r 9
 
3.8%
Other values (15) 51
21.5%
Decimal Number
ValueCountFrequency (%)
1 12709
19.6%
2 7766
12.0%
0 7549
11.7%
4 6060
9.4%
3 5902
9.1%
6 5652
8.7%
5 5644
8.7%
7 4631
 
7.2%
8 4483
 
6.9%
9 4351
 
6.7%
Space Separator
ValueCountFrequency (%)
665
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 109728
62.6%
Common 65438
37.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 24876
22.7%
A 12813
11.7%
E 11963
10.9%
H 11780
10.7%
X 5973
 
5.4%
R 5331
 
4.9%
C 5186
 
4.7%
T 4203
 
3.8%
B 3639
 
3.3%
D 3315
 
3.0%
Other values (42) 20649
18.8%
Common
ValueCountFrequency (%)
1 12709
19.4%
2 7766
11.9%
0 7549
11.5%
4 6060
9.3%
3 5902
9.0%
6 5652
8.6%
5 5644
8.6%
7 4631
 
7.1%
8 4483
 
6.9%
9 4351
 
6.6%
Other values (3) 691
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 175160
> 99.9%
None 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 24876
 
14.2%
A 12813
 
7.3%
1 12709
 
7.3%
E 11963
 
6.8%
H 11780
 
6.7%
2 7766
 
4.4%
0 7549
 
4.3%
4 6060
 
3.5%
X 5973
 
3.4%
3 5902
 
3.4%
Other values (51) 67769
38.7%
None
ValueCountFrequency (%)
Ä 3
50.0%
è 1
 
16.7%
é 1
 
16.7%
à 1
 
16.7%

Article
Text

Distinct18922
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-03-09T15:51:46.956317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length60
Median length50
Mean length10.516817
Min length0

Characters and Unicode

Total characters245147
Distinct characters83
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17062 ?
Unique (%)73.2%

Sample

1st rowCITY P1 BLACK
2nd rowReport à Nouveau
3rd rowLOUISON C1 LOUISON NOIR
4th rowNATALIA ECRU LUREX LIGHT GOLD
5th rowNANNI GRIS CHAINE SILVER LUREX-2002
ValueCountFrequency (%)
r1 3052
 
6.3%
c1 2115
 
4.3%
j1 1411
 
2.9%
p1 1303
 
2.7%
ml 1022
 
2.1%
bo 930
 
1.9%
t1 892
 
1.8%
mc 885
 
1.8%
noir 849
 
1.7%
v1 537
 
1.1%
Other values (11374) 35660
73.3%
2025-03-09T15:51:47.298104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27109
 
11.1%
E 21525
 
8.8%
A 21219
 
8.7%
R 16415
 
6.7%
I 15598
 
6.4%
L 14402
 
5.9%
O 13966
 
5.7%
N 12050
 
4.9%
1 11290
 
4.6%
C 10031
 
4.1%
Other values (73) 81542
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 201732
82.3%
Space Separator 27109
 
11.1%
Decimal Number 13216
 
5.4%
Lowercase Letter 1688
 
0.7%
Dash Punctuation 898
 
0.4%
Other Punctuation 475
 
0.2%
Open Punctuation 12
 
< 0.1%
Close Punctuation 12
 
< 0.1%
Math Symbol 4
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 21525
 
10.7%
A 21219
 
10.5%
R 16415
 
8.1%
I 15598
 
7.7%
L 14402
 
7.1%
O 13966
 
6.9%
N 12050
 
6.0%
C 10031
 
5.0%
M 9986
 
5.0%
S 9515
 
4.7%
Other values (20) 57025
28.3%
Lowercase Letter
ValueCountFrequency (%)
e 266
15.8%
a 144
 
8.5%
o 118
 
7.0%
n 114
 
6.8%
r 114
 
6.8%
l 111
 
6.6%
t 110
 
6.5%
c 94
 
5.6%
m 88
 
5.2%
i 86
 
5.1%
Other values (18) 443
26.2%
Decimal Number
ValueCountFrequency (%)
1 11290
85.4%
2 1131
 
8.6%
3 257
 
1.9%
0 154
 
1.2%
4 119
 
0.9%
7 61
 
0.5%
5 61
 
0.5%
8 55
 
0.4%
6 48
 
0.4%
9 40
 
0.3%
Other Punctuation
ValueCountFrequency (%)
/ 396
83.4%
' 27
 
5.7%
. 21
 
4.4%
, 12
 
2.5%
% 11
 
2.3%
& 7
 
1.5%
? 1
 
0.2%
Open Punctuation
ValueCountFrequency (%)
[ 7
58.3%
( 5
41.7%
Close Punctuation
ValueCountFrequency (%)
] 7
58.3%
) 5
41.7%
Space Separator
ValueCountFrequency (%)
27109
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 898
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 203420
83.0%
Common 41727
 
17.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 21525
 
10.6%
A 21219
 
10.4%
R 16415
 
8.1%
I 15598
 
7.7%
L 14402
 
7.1%
O 13966
 
6.9%
N 12050
 
5.9%
C 10031
 
4.9%
M 9986
 
4.9%
S 9515
 
4.7%
Other values (48) 58713
28.9%
Common
ValueCountFrequency (%)
27109
65.0%
1 11290
27.1%
2 1131
 
2.7%
- 898
 
2.2%
/ 396
 
0.9%
3 257
 
0.6%
0 154
 
0.4%
4 119
 
0.3%
7 61
 
0.1%
5 61
 
0.1%
Other values (15) 251
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 245093
> 99.9%
None 54
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27109
 
11.1%
E 21525
 
8.8%
A 21219
 
8.7%
R 16415
 
6.7%
I 15598
 
6.4%
L 14402
 
5.9%
O 13966
 
5.7%
N 12050
 
4.9%
1 11290
 
4.6%
C 10031
 
4.1%
Other values (66) 81488
33.2%
None
ValueCountFrequency (%)
é 30
55.6%
à 8
 
14.8%
É 7
 
13.0%
À 3
 
5.6%
Π3
 
5.6%
ê 2
 
3.7%
Ç 1
 
1.9%

IDAr_Couleur
Real number (ℝ)

High correlation  Zeros 

Distinct1632
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean292.13445
Minimum0
Maximum2123
Zeros7245
Zeros (%)31.1%
Negative0
Negative (%)0.0%
Memory size182.2 KiB
2025-03-09T15:51:47.378908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median156
Q3275
95-th percentile1303
Maximum2123
Range2123
Interquartile range (IQR)275

Descriptive statistics

Standard deviation416.06264
Coefficient of variation (CV)1.4242163
Kurtosis4.7116407
Mean292.13445
Median Absolute Deviation (MAD)156
Skewness2.1991607
Sum6809654
Variance173108.12
MonotonicityNot monotonic
2025-03-09T15:51:47.472811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7245
31.1%
122 2395
 
10.3%
275 1512
 
6.5%
156 1088
 
4.7%
191 815
 
3.5%
127 736
 
3.2%
201 337
 
1.4%
90 168
 
0.7%
190 149
 
0.6%
244 141
 
0.6%
Other values (1622) 8724
37.4%
ValueCountFrequency (%)
0 7245
31.1%
2 13
 
0.1%
3 5
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
15 2
 
< 0.1%
16 1
 
< 0.1%
ValueCountFrequency (%)
2123 1
 
< 0.1%
2122 1
 
< 0.1%
2121 1
 
< 0.1%
2120 1
 
< 0.1%
2119 1
 
< 0.1%
2118 2
< 0.1%
2117 3
< 0.1%
2116 1
 
< 0.1%
2115 1
 
< 0.1%
2114 3
< 0.1%

IDArFamille
Real number (ℝ)

High correlation 

Distinct41
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.771729
Minimum-1
Maximum56
Zeros220
Zeros (%)0.9%
Negative10
Negative (%)< 0.1%
Memory size182.2 KiB
2025-03-09T15:51:47.557329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile2
Q14
median6
Q327
95-th percentile38
Maximum56
Range57
Interquartile range (IQR)23

Descriptive statistics

Standard deviation14.243635
Coefficient of variation (CV)1.0342663
Kurtosis-0.78824449
Mean13.771729
Median Absolute Deviation (MAD)4
Skewness0.93480945
Sum321019
Variance202.88113
MonotonicityNot monotonic
2025-03-09T15:51:47.639784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
4 3742
16.1%
6 3100
13.3%
2 2677
11.5%
3 2003
8.6%
38 1765
 
7.6%
5 1639
 
7.0%
10 1529
 
6.6%
25 881
 
3.8%
34 729
 
3.1%
37 663
 
2.8%
Other values (31) 4582
19.7%
ValueCountFrequency (%)
-1 10
 
< 0.1%
0 220
 
0.9%
1 537
 
2.3%
2 2677
11.5%
3 2003
8.6%
4 3742
16.1%
5 1639
7.0%
6 3100
13.3%
7 498
 
2.1%
8 44
 
0.2%
ValueCountFrequency (%)
56 1
 
< 0.1%
54 16
 
0.1%
52 1
 
< 0.1%
51 7
 
< 0.1%
49 216
0.9%
48 7
 
< 0.1%
47 11
 
< 0.1%
46 141
0.6%
45 11
 
< 0.1%
44 70
 
0.3%

SaisiPar
Categorical

High correlation  Imbalance 

Distinct39
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
16321 
superviseur
1706 
UP.Ceren
 
1058
IBTISSEM
 
585
florian.desvign
 
477
Other values (34)
3163 

Length

Max length15
Median length0
Mean length2.9615187
Min length0

Characters and Unicode

Total characters69033
Distinct characters48
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowsuperviseur
2nd row
3rd rowsuperviseur
4th rowsuperviseur
5th rowsuperviseur

Common Values

ValueCountFrequency (%)
16321
70.0%
superviseur 1706
 
7.3%
UP.Ceren 1058
 
4.5%
IBTISSEM 585
 
2.5%
florian.desvign 477
 
2.0%
K.tuba 467
 
2.0%
soraia pereira 392
 
1.7%
B.zuhal 385
 
1.7%
ibtissem 339
 
1.5%
s.hayriye 280
 
1.2%
Other values (29) 1300
 
5.6%

Length

2025-03-09T15:51:47.734470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
superviseur 1732
22.7%
up.ceren 1058
13.9%
ibtissem 924
12.1%
pereira 533
 
7.0%
soraia 533
 
7.0%
b.zuhal 510
 
6.7%
k.tuba 501
 
6.6%
florian.desvign 477
 
6.2%
s.hayriye 280
 
3.7%
yasmine 187
 
2.5%
Other values (19) 897
11.8%

Most occurring characters

ValueCountFrequency (%)
e 8391
 
12.2%
r 6698
 
9.7%
s 5762
 
8.3%
i 5471
 
7.9%
u 4925
 
7.1%
a 4749
 
6.9%
. 3411
 
4.9%
n 2811
 
4.1%
v 2198
 
3.2%
p 2157
 
3.1%
Other values (38) 22460
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53636
77.7%
Uppercase Letter 11315
 
16.4%
Other Punctuation 3411
 
4.9%
Space Separator 671
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8391
15.6%
r 6698
12.5%
s 5762
10.7%
i 5471
10.2%
u 4925
9.2%
a 4749
8.9%
n 2811
 
5.2%
v 2198
 
4.1%
p 2157
 
4.0%
l 1477
 
2.8%
Other values (14) 8997
16.8%
Uppercase Letter
ValueCountFrequency (%)
S 1421
12.6%
I 1419
12.5%
P 1320
11.7%
B 1175
10.4%
U 1108
9.8%
C 1103
9.7%
E 939
8.3%
M 764
6.8%
T 585
5.2%
K 467
 
4.1%
Other values (11) 1014
9.0%
Space Separator
ValueCountFrequency (%)
629
93.7%
  42
 
6.3%
Other Punctuation
ValueCountFrequency (%)
. 3411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 64951
94.1%
Common 4082
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8391
12.9%
r 6698
 
10.3%
s 5762
 
8.9%
i 5471
 
8.4%
u 4925
 
7.6%
a 4749
 
7.3%
n 2811
 
4.3%
v 2198
 
3.4%
p 2157
 
3.3%
l 1477
 
2.3%
Other values (35) 20312
31.3%
Common
ValueCountFrequency (%)
. 3411
83.6%
629
 
15.4%
  42
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68991
99.9%
None 42
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8391
 
12.2%
r 6698
 
9.7%
s 5762
 
8.4%
i 5471
 
7.9%
u 4925
 
7.1%
a 4749
 
6.9%
. 3411
 
4.9%
n 2811
 
4.1%
v 2198
 
3.2%
p 2157
 
3.1%
Other values (37) 22418
32.5%
None
ValueCountFrequency (%)
  42
100.0%

SaisiLe
Date

Distinct317
Distinct (%)1.4%
Missing154
Missing (%)0.7%
Memory size909.5 KiB
Minimum2023-11-02 00:00:00
Maximum2025-02-28 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-09T15:51:47.828221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:47.926190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ModifiePar
Categorical

High correlation  Imbalance 

Distinct40
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
16681 
superviseur
 
1068
UP.Ceren
 
845
H.HANA
 
547
IBTISSEM
 
476
Other values (35)
3693 

Length

Max length15
Median length0
Mean length2.7018018
Min length0

Characters and Unicode

Total characters62979
Distinct characters50
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowsuperviseur
2nd row
3rd rowsuperviseur
4th rowsuperviseur
5th rowsuperviseur

Common Values

ValueCountFrequency (%)
16681
71.6%
superviseur 1068
 
4.6%
UP.Ceren 845
 
3.6%
H.HANA 547
 
2.3%
IBTISSEM 476
 
2.0%
s.hayriye 458
 
2.0%
Poobahdee GOVIN 421
 
1.8%
Pooba 406
 
1.7%
florian.desvign 354
 
1.5%
K.tuba 298
 
1.3%
Other values (30) 1756
 
7.5%

Length

2025-03-09T15:51:48.023182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
superviseur 1109
14.6%
up.ceren 845
11.1%
ibtissem 632
 
8.3%
h.hana 547
 
7.2%
s.hayriye 458
 
6.0%
b.zuhal 427
 
5.6%
poobahdee 421
 
5.5%
govin 421
 
5.5%
pooba 406
 
5.3%
florian.desvign 354
 
4.7%
Other values (23) 1973
26.0%

Most occurring characters

ValueCountFrequency (%)
e 6693
 
10.6%
r 4681
 
7.4%
a 4645
 
7.4%
s 3812
 
6.1%
i 3738
 
5.9%
u 3536
 
5.6%
. 3377
 
5.4%
o 2712
 
4.3%
n 2345
 
3.7%
P 1871
 
3.0%
Other values (40) 25569
40.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43938
69.8%
Uppercase Letter 14678
 
23.3%
Other Punctuation 3377
 
5.4%
Space Separator 986
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6693
15.2%
r 4681
10.7%
a 4645
10.6%
s 3812
8.7%
i 3738
8.5%
u 3536
8.0%
o 2712
 
6.2%
n 2345
 
5.3%
b 1632
 
3.7%
h 1590
 
3.6%
Other values (16) 8554
19.5%
Uppercase Letter
ValueCountFrequency (%)
P 1871
12.7%
I 1523
10.4%
A 1204
 
8.2%
S 1165
 
7.9%
H 1094
 
7.5%
N 968
 
6.6%
U 925
 
6.3%
B 867
 
5.9%
C 859
 
5.9%
E 762
 
5.2%
Other values (11) 3440
23.4%
Space Separator
ValueCountFrequency (%)
565
57.3%
  421
42.7%
Other Punctuation
ValueCountFrequency (%)
. 3377
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 58616
93.1%
Common 4363
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6693
 
11.4%
r 4681
 
8.0%
a 4645
 
7.9%
s 3812
 
6.5%
i 3738
 
6.4%
u 3536
 
6.0%
o 2712
 
4.6%
n 2345
 
4.0%
P 1871
 
3.2%
b 1632
 
2.8%
Other values (37) 22951
39.2%
Common
ValueCountFrequency (%)
. 3377
77.4%
565
 
12.9%
  421
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62557
99.3%
None 422
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6693
 
10.7%
r 4681
 
7.5%
a 4645
 
7.4%
s 3812
 
6.1%
i 3738
 
6.0%
u 3536
 
5.7%
. 3377
 
5.4%
o 2712
 
4.3%
n 2345
 
3.7%
P 1871
 
3.0%
Other values (38) 25147
40.2%
None
ValueCountFrequency (%)
  421
99.8%
ä 1
 
0.2%

ModifieLe
Date

Missing 

Distinct304
Distinct (%)4.3%
Missing16304
Missing (%)69.9%
Memory size783.3 KiB
Minimum2023-10-31 00:00:00
Maximum2025-02-28 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-09T15:51:48.116935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:48.226249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Etat
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
23005 
2
 
219
0
 
59
3
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23005
98.7%
2 219
 
0.9%
0 59
 
0.3%
3 27
 
0.1%

Length

2025-03-09T15:51:48.314282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:48.362349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 23005
98.7%
2 219
 
0.9%
0 59
 
0.3%
3 27
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 23005
98.7%
2 219
 
0.9%
0 59
 
0.3%
3 27
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 23005
98.7%
2 219
 
0.9%
0 59
 
0.3%
3 27
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 23005
98.7%
2 219
 
0.9%
0 59
 
0.3%
3 27
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 23005
98.7%
2 219
 
0.9%
0 59
 
0.3%
3 27
 
0.1%

IDGamme
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:48.410744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:48.457620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

IDClient
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:48.516456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:48.559190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

TempsClient
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:51:48.607326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:48.654272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Prix
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct921
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.097714
Minimum0
Maximum571428.57
Zeros4974
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size182.2 KiB
2025-03-09T15:51:48.702562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6.5151
Q311.43
95-th percentile22.85
Maximum571428.57
Range571428.57
Interquartile range (IQR)10.43

Descriptive statistics

Standard deviation5430.4125
Coefficient of variation (CV)83.419404
Kurtosis10573.407
Mean65.097714
Median Absolute Deviation (MAD)4.9149
Skewness101.65775
Sum1517427.7
Variance29489380
MonotonicityNot monotonic
2025-03-09T15:51:48.800199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4974
 
21.3%
14.28 770
 
3.3%
11.43 689
 
3.0%
2.83 604
 
2.6%
12.85 517
 
2.2%
8.57 498
 
2.1%
17.14 478
 
2.1%
0.29 447
 
1.9%
20 422
 
1.8%
2.26 384
 
1.6%
Other values (911) 13527
58.0%
ValueCountFrequency (%)
0 4974
21.3%
0.01 142
 
0.6%
0.03 2
 
< 0.1%
0.1 1
 
< 0.1%
0.15 1
 
< 0.1%
0.17 4
 
< 0.1%
0.2 1
 
< 0.1%
0.25 1
 
< 0.1%
0.29 447
 
1.9%
0.3 1
 
< 0.1%
ValueCountFrequency (%)
571428.57 2
 
< 0.1%
185478.55 1
 
< 0.1%
94.2 2
 
< 0.1%
89.99 1
 
< 0.1%
88.8 1
 
< 0.1%
85.71 1
 
< 0.1%
80 1
 
< 0.1%
71.43 1
 
< 0.1%
65.71 1
 
< 0.1%
57.14 5
< 0.1%

TauxTVA
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
19362 
-1.0
2893 
0.2
 
1043
0.21
 
12

Length

Max length4
Median length3
Mean length3.1246246
Min length3

Characters and Unicode

Total characters72835
Distinct characters5
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row-1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 19362
83.1%
-1.0 2893
 
12.4%
0.2 1043
 
4.5%
0.21 12
 
0.1%

Length

2025-03-09T15:51:48.895568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:48.937016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 19362
83.1%
1.0 2893
 
12.4%
0.2 1043
 
4.5%
0.21 12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 42672
58.6%
. 23310
32.0%
1 2905
 
4.0%
- 2893
 
4.0%
2 1055
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46632
64.0%
Other Punctuation 23310
32.0%
Dash Punctuation 2893
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42672
91.5%
1 2905
 
6.2%
2 1055
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2893
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 42672
58.6%
. 23310
32.0%
1 2905
 
4.0%
- 2893
 
4.0%
2 1055
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 42672
58.6%
. 23310
32.0%
1 2905
 
4.0%
- 2893
 
4.0%
2 1055
 
1.4%

Image
Unsupported

Missing  Rejected  Unsupported 

Missing23310
Missing (%)100.0%
Memory size182.2 KiB

IdProcess
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:49.000920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:49.049991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

prixMP
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:51:49.096871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:49.129395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

CodeDouane
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
23290 
chaussure
 
19
DDP
 
1

Length

Max length10
Median length0
Mean length0.0082797083
Min length0

Characters and Unicode

Total characters193
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
23290
99.9%
chaussure 19
 
0.1%
DDP 1
 
< 0.1%

Length

2025-03-09T15:51:49.176422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:49.223299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
chaussure 19
95.0%
ddp 1
 
5.0%

Most occurring characters

ValueCountFrequency (%)
s 38
19.7%
u 38
19.7%
h 19
9.8%
c 19
9.8%
a 19
9.8%
r 19
9.8%
e 19
9.8%
19
9.8%
D 2
 
1.0%
P 1
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 171
88.6%
Space Separator 19
 
9.8%
Uppercase Letter 3
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 38
22.2%
u 38
22.2%
h 19
11.1%
c 19
11.1%
a 19
11.1%
r 19
11.1%
e 19
11.1%
Uppercase Letter
ValueCountFrequency (%)
D 2
66.7%
P 1
33.3%
Space Separator
ValueCountFrequency (%)
19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 174
90.2%
Common 19
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 38
21.8%
u 38
21.8%
h 19
10.9%
c 19
10.9%
a 19
10.9%
r 19
10.9%
e 19
10.9%
D 2
 
1.1%
P 1
 
0.6%
Common
ValueCountFrequency (%)
19
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 193
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 38
19.7%
u 38
19.7%
h 19
9.8%
c 19
9.8%
a 19
9.8%
r 19
9.8%
e 19
9.8%
19
9.8%
D 2
 
1.0%
P 1
 
0.5%

Composition
Text

Distinct586
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2025-03-09T15:51:49.432583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length49
Mean length5.7497211
Min length0

Characters and Unicode

Total characters134026
Distinct characters81
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique173 ?
Unique (%)0.7%

Sample

1st row96% POLYESTER 4% ELASTANE WOVEN
2nd row
3rd rowTP:100% PES
4th row66%Acrylique 18%Polyamide 8%Laine
5th row40% ACR 30% PA 30% MOHAIR
ValueCountFrequency (%)
polyester 4373
22.0%
coton 2090
 
10.5%
viscose 1972
 
9.9%
100 821
 
4.1%
polyurethane 675
 
3.4%
acrylique 647
 
3.3%
pes 455
 
2.3%
polyamide 290
 
1.5%
cuir 286
 
1.4%
laine 258
 
1.3%
Other values (548) 8009
40.3%
2025-03-09T15:51:49.763332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 16863
 
12.6%
O 13349
 
10.0%
S 10239
 
7.6%
L 8457
 
6.3%
T 8323
 
6.2%
P 7099
 
5.3%
6959
 
5.2%
R 6866
 
5.1%
Y 6519
 
4.9%
C 6475
 
4.8%
Other values (71) 42877
32.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 106846
79.7%
Decimal Number 11565
 
8.6%
Space Separator 6978
 
5.2%
Other Punctuation 6186
 
4.6%
Lowercase Letter 1665
 
1.2%
Control 502
 
0.4%
Dash Punctuation 275
 
0.2%
Other Symbol 4
 
< 0.1%
Math Symbol 4
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 16863
15.8%
O 13349
12.5%
S 10239
9.6%
L 8457
7.9%
T 8323
7.8%
P 7099
 
6.6%
R 6866
 
6.4%
Y 6519
 
6.1%
C 6475
 
6.1%
I 4893
 
4.6%
Other values (18) 17763
16.6%
Lowercase Letter
ValueCountFrequency (%)
l 180
10.8%
o 179
10.8%
e 174
10.5%
y 162
9.7%
i 123
 
7.4%
c 121
 
7.3%
r 119
 
7.1%
n 110
 
6.6%
a 86
 
5.2%
s 79
 
4.7%
Other values (15) 332
19.9%
Decimal Number
ValueCountFrequency (%)
0 3983
34.4%
1 2028
17.5%
5 954
 
8.2%
2 944
 
8.2%
4 850
 
7.3%
3 676
 
5.8%
8 609
 
5.3%
9 581
 
5.0%
6 471
 
4.1%
7 469
 
4.1%
Other Punctuation
ValueCountFrequency (%)
% 5511
89.1%
, 353
 
5.7%
/ 153
 
2.5%
: 92
 
1.5%
? 53
 
0.9%
. 14
 
0.2%
" 4
 
0.1%
& 4
 
0.1%
; 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
6959
99.7%
  19
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 270
98.2%
– 5
 
1.8%
Control
ValueCountFrequency (%)
254
50.6%
248
49.4%
Other Symbol
ValueCountFrequency (%)
° 4
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%
Modifier Symbol
ValueCountFrequency (%)
¨ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 108511
81.0%
Common 25515
 
19.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 16863
15.5%
O 13349
12.3%
S 10239
9.4%
L 8457
7.8%
T 8323
7.7%
P 7099
 
6.5%
R 6866
 
6.3%
Y 6519
 
6.0%
C 6475
 
6.0%
I 4893
 
4.5%
Other values (43) 19428
17.9%
Common
ValueCountFrequency (%)
6959
27.3%
% 5511
21.6%
0 3983
15.6%
1 2028
 
7.9%
5 954
 
3.7%
2 944
 
3.7%
4 850
 
3.3%
3 676
 
2.6%
8 609
 
2.4%
9 581
 
2.3%
Other values (18) 2420
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 133713
99.8%
None 308
 
0.2%
Punctuation 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 16863
 
12.6%
O 13349
 
10.0%
S 10239
 
7.7%
L 8457
 
6.3%
T 8323
 
6.2%
P 7099
 
5.3%
6959
 
5.2%
R 6866
 
5.1%
Y 6519
 
4.9%
C 6475
 
4.8%
Other values (63) 42564
31.8%
None
ValueCountFrequency (%)
È 274
89.0%
  19
 
6.2%
É 7
 
2.3%
° 4
 
1.3%
ê 2
 
0.6%
é 1
 
0.3%
¨ 1
 
0.3%
Punctuation
ValueCountFrequency (%)
– 5
100.0%

Valeur
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:51:49.833460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:49.869972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

PrixFac
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct242
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.05804
Minimum0
Maximum2000000
Zeros3136
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size182.2 KiB
2025-03-09T15:51:49.934905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114.9
median36.99
Q354.99
95-th percentile89.99
Maximum2000000
Range2000000
Interquartile range (IQR)40.09

Descriptive statistics

Standard deviation19006.333
Coefficient of variation (CV)79.505098
Kurtosis10573.413
Mean239.05804
Median Absolute Deviation (MAD)20
Skewness101.65776
Sum5572443
Variance3.612407 × 108
MonotonicityNot monotonic
2025-03-09T15:51:50.030209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3136
 
13.5%
49.99 1496
 
6.4%
39.99 1419
 
6.1%
59.99 973
 
4.2%
44.99 971
 
4.2%
29.99 959
 
4.1%
69.99 819
 
3.5%
19.99 720
 
3.1%
1 710
 
3.0%
54.99 678
 
2.9%
Other values (232) 11429
49.0%
ValueCountFrequency (%)
0 3136
13.5%
0.01 151
 
0.6%
0.1 2
 
< 0.1%
0.15 1
 
< 0.1%
0.2 1
 
< 0.1%
0.25 1
 
< 0.1%
0.5 1
 
< 0.1%
0.6 1
 
< 0.1%
0.8 1
 
< 0.1%
1 710
 
3.0%
ValueCountFrequency (%)
2000000 2
< 0.1%
649174.94 1
 
< 0.1%
350 1
 
< 0.1%
299.99 2
< 0.1%
280 1
 
< 0.1%
260 1
 
< 0.1%
249.99 3
< 0.1%
245 1
 
< 0.1%
239.99 1
 
< 0.1%
229 3
< 0.1%

Cadence
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:51:50.108334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:50.155213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

IdArticleBase
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:50.202085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:50.233335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

SemiFini
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:50.291377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:50.322629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

PoidsBrut
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0072994423
Minimum0
Maximum99
Zeros23241
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size182.2 KiB
2025-03-09T15:51:50.366257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum99
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.65372699
Coefficient of variation (CV)89.558485
Kurtosis22563.311
Mean0.0072994423
Median Absolute Deviation (MAD)0
Skewness149.14507
Sum170.15
Variance0.42735898
MonotonicityNot monotonic
2025-03-09T15:51:50.433672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 23241
99.7%
1 57
 
0.2%
0.25 4
 
< 0.1%
0.3 3
 
< 0.1%
2 1
 
< 0.1%
0.15 1
 
< 0.1%
10 1
 
< 0.1%
99 1
 
< 0.1%
0.1 1
 
< 0.1%
ValueCountFrequency (%)
0 23241
99.7%
0.1 1
 
< 0.1%
0.15 1
 
< 0.1%
0.25 4
 
< 0.1%
0.3 3
 
< 0.1%
1 57
 
0.2%
2 1
 
< 0.1%
10 1
 
< 0.1%
99 1
 
< 0.1%
ValueCountFrequency (%)
99 1
 
< 0.1%
10 1
 
< 0.1%
2 1
 
< 0.1%
1 57
 
0.2%
0.3 3
 
< 0.1%
0.25 4
 
< 0.1%
0.15 1
 
< 0.1%
0.1 1
 
< 0.1%
0 23241
99.7%

PoidsNet
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct299
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24104492
Minimum0
Maximum810
Zeros8493
Zeros (%)36.4%
Negative0
Negative (%)0.0%
Memory size182.2 KiB
2025-03-09T15:51:50.515195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1
Q30.3
95-th percentile0.6
Maximum810
Range810
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation6.3446782
Coefficient of variation (CV)26.32156
Kurtosis13468.804
Mean0.24104492
Median Absolute Deviation (MAD)0.1
Skewness114.17552
Sum5618.757
Variance40.254941
MonotonicityNot monotonic
2025-03-09T15:51:50.634195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8493
36.4%
0.2 1430
 
6.1%
0.1 1287
 
5.5%
0.15 1118
 
4.8%
0.35 734
 
3.1%
0.25 676
 
2.9%
0.3 623
 
2.7%
0.4 425
 
1.8%
0.45 297
 
1.3%
0.05 257
 
1.1%
Other values (289) 7970
34.2%
ValueCountFrequency (%)
0 8493
36.4%
0.001 22
 
0.1%
0.002 20
 
0.1%
0.003 51
 
0.2%
0.004 100
 
0.4%
0.005 162
 
0.7%
0.006 121
 
0.5%
0.007 92
 
0.4%
0.008 112
 
0.5%
0.009 67
 
0.3%
ValueCountFrequency (%)
810 1
< 0.1%
530 1
< 0.1%
24 1
< 0.1%
1.83 1
< 0.1%
1.81 1
< 0.1%
1.8 1
< 0.1%
1.75 1
< 0.1%
1.74 1
< 0.1%
1.73 2
< 0.1%
1.72 2
< 0.1%

ValeurTissu
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:51:50.739851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:50.788866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

ValeurFourniture
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:51:50.848840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:50.882599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

ValeurMP
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:51:50.949703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:50.990621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

TypeTarif
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:51.045380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:51.094727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

IDArSousFamille
Real number (ℝ)

High correlation  Zeros 

Distinct126
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.60845
Minimum-1
Maximum336
Zeros464
Zeros (%)2.0%
Negative280
Negative (%)1.2%
Memory size182.2 KiB
2025-03-09T15:51:51.354114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile4
Q1195
median211
Q3221
95-th percentile282
Maximum336
Range337
Interquartile range (IQR)26

Descriptive statistics

Standard deviation74.809741
Coefficient of variation (CV)0.38840321
Kurtosis2.3362425
Mean192.60845
Median Absolute Deviation (MAD)12
Skewness-1.7903168
Sum4489703
Variance5596.4973
MonotonicityNot monotonic
2025-03-09T15:51:51.465844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
211 2264
 
9.7%
4 1498
 
6.4%
201 1386
 
5.9%
216 1386
 
5.9%
191 1336
 
5.7%
221 1277
 
5.5%
212 863
 
3.7%
218 688
 
3.0%
223 610
 
2.6%
208 609
 
2.6%
Other values (116) 11393
48.9%
ValueCountFrequency (%)
-1 280
 
1.2%
0 464
 
2.0%
1 379
 
1.6%
4 1498
6.4%
5 19
 
0.1%
6 1
 
< 0.1%
7 29
 
0.1%
8 178
 
0.8%
9 3
 
< 0.1%
190 491
 
2.1%
ValueCountFrequency (%)
336 1
 
< 0.1%
335 1
 
< 0.1%
333 5
 
< 0.1%
332 1
 
< 0.1%
331 2
 
< 0.1%
329 14
0.1%
325 24
0.1%
323 4
 
< 0.1%
322 2
 
< 0.1%
321 14
0.1%

IdMeilleurOF
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:51.552953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:51.599176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

IDGrille
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7240669
Minimum0
Maximum11
Zeros430
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size182.2 KiB
2025-03-09T15:51:51.642963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0201791
Coefficient of variation (CV)0.59172822
Kurtosis9.299234
Mean1.7240669
Median Absolute Deviation (MAD)1
Skewness2.1638312
Sum40188
Variance1.0407653
MonotonicityNot monotonic
2025-03-09T15:51:51.717659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 11485
49.3%
2 7030
30.2%
3 3727
 
16.0%
0 430
 
1.8%
4 320
 
1.4%
7 192
 
0.8%
6 100
 
0.4%
10 18
 
0.1%
5 4
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 430
 
1.8%
1 11485
49.3%
2 7030
30.2%
3 3727
 
16.0%
4 320
 
1.4%
5 4
 
< 0.1%
6 100
 
0.4%
7 192
 
0.8%
8 2
 
< 0.1%
10 18
 
0.1%
ValueCountFrequency (%)
11 2
 
< 0.1%
10 18
 
0.1%
8 2
 
< 0.1%
7 192
 
0.8%
6 100
 
0.4%
5 4
 
< 0.1%
4 320
 
1.4%
3 3727
 
16.0%
2 7030
30.2%
1 11485
49.3%

Reference
Text

Distinct4542
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2025-03-09T15:51:51.957386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length0
Mean length2.438181
Min length0

Characters and Unicode

Total characters56834
Distinct characters54
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3186 ?
Unique (%)13.7%

Sample

1st row42624_24K
2nd row
3rd rowLOUISON C1
4th rowNATALIA
5th rowAENU102A
ValueCountFrequency (%)
c1 417
 
4.3%
r1 396
 
4.1%
t1 192
 
2.0%
p1 189
 
2.0%
j1 166
 
1.7%
ml 94
 
1.0%
v1 93
 
1.0%
sh1 85
 
0.9%
d1 63
 
0.7%
g1 57
 
0.6%
Other values (4133) 7903
81.9%
2025-03-09T15:51:52.296892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 5905
 
10.4%
E 4048
 
7.1%
1 3961
 
7.0%
C 3251
 
5.7%
A 3250
 
5.7%
H 3016
 
5.3%
0 2634
 
4.6%
2491
 
4.4%
R 2209
 
3.9%
B 2011
 
3.5%
Other values (44) 24058
42.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 37142
65.4%
Decimal Number 16845
29.6%
Space Separator 2491
 
4.4%
Dash Punctuation 246
 
0.4%
Lowercase Letter 68
 
0.1%
Connector Punctuation 41
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 5905
15.9%
E 4048
10.9%
C 3251
 
8.8%
A 3250
 
8.8%
H 3016
 
8.1%
R 2209
 
5.9%
B 2011
 
5.4%
L 1407
 
3.8%
D 1375
 
3.7%
T 1367
 
3.7%
Other values (17) 9303
25.0%
Lowercase Letter
ValueCountFrequency (%)
e 16
23.5%
t 16
23.5%
s 13
19.1%
a 5
 
7.4%
n 4
 
5.9%
o 3
 
4.4%
h 2
 
2.9%
r 2
 
2.9%
l 2
 
2.9%
b 2
 
2.9%
Other values (3) 3
 
4.4%
Decimal Number
ValueCountFrequency (%)
1 3961
23.5%
0 2634
15.6%
2 1995
11.8%
4 1676
9.9%
3 1302
 
7.7%
5 1255
 
7.5%
6 1169
 
6.9%
7 975
 
5.8%
9 939
 
5.6%
8 939
 
5.6%
Space Separator
ValueCountFrequency (%)
2491
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 246
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 41
100.0%
Other Punctuation
ValueCountFrequency (%)
? 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37210
65.5%
Common 19624
34.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 5905
15.9%
E 4048
10.9%
C 3251
 
8.7%
A 3250
 
8.7%
H 3016
 
8.1%
R 2209
 
5.9%
B 2011
 
5.4%
L 1407
 
3.8%
D 1375
 
3.7%
T 1367
 
3.7%
Other values (30) 9371
25.2%
Common
ValueCountFrequency (%)
1 3961
20.2%
0 2634
13.4%
2491
12.7%
2 1995
10.2%
4 1676
8.5%
3 1302
 
6.6%
5 1255
 
6.4%
6 1169
 
6.0%
7 975
 
5.0%
9 939
 
4.8%
Other values (4) 1227
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56832
> 99.9%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 5905
 
10.4%
E 4048
 
7.1%
1 3961
 
7.0%
C 3251
 
5.7%
A 3250
 
5.7%
H 3016
 
5.3%
0 2634
 
4.6%
2491
 
4.4%
R 2209
 
3.9%
B 2011
 
3.5%
Other values (43) 24056
42.3%
None
ValueCountFrequency (%)
Ë 2
100.0%

NomenclatureValide
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23307 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23307
> 99.9%
1 3
 
< 0.1%

Length

2025-03-09T15:51:52.382571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:52.446223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23307
> 99.9%
1 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 23307
> 99.9%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23307
> 99.9%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23307
> 99.9%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23307
> 99.9%
1 3
 
< 0.1%

IDSaison
Real number (ℝ)

High correlation  Zeros 

Distinct32
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.584728
Minimum0
Maximum34
Zeros767
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size182.2 KiB
2025-03-09T15:51:52.498235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median16
Q321
95-th percentile28
Maximum34
Range34
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.494012
Coefficient of variation (CV)0.69887393
Kurtosis-1.5332973
Mean13.584728
Median Absolute Deviation (MAD)11
Skewness-0.0010824665
Sum316660
Variance90.136263
MonotonicityNot monotonic
2025-03-09T15:51:52.616380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
4 2956
12.7%
5 2061
 
8.8%
1 1752
 
7.5%
19 1599
 
6.9%
18 1561
 
6.7%
22 1534
 
6.6%
28 1504
 
6.5%
3 1477
 
6.3%
15 1421
 
6.1%
20 1410
 
6.0%
Other values (22) 6035
25.9%
ValueCountFrequency (%)
0 767
 
3.3%
1 1752
7.5%
2 160
 
0.7%
3 1477
6.3%
4 2956
12.7%
5 2061
8.8%
6 887
 
3.8%
8 1
 
< 0.1%
11 14
 
0.1%
12 16
 
0.1%
ValueCountFrequency (%)
34 62
 
0.3%
33 4
 
< 0.1%
32 4
 
< 0.1%
31 2
 
< 0.1%
30 2
 
< 0.1%
29 7
 
< 0.1%
28 1504
6.5%
27 1178
5.1%
26 2
 
< 0.1%
25 1
 
< 0.1%

NumInterne
Real number (ℝ)

High correlation  Zeros 

Distinct283
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.572287
Minimum0
Maximum282
Zeros15121
Zeros (%)64.9%
Negative0
Negative (%)0.0%
Memory size182.2 KiB
2025-03-09T15:51:52.793622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q355
95-th percentile145
Maximum282
Range282
Interquartile range (IQR)55

Descriptive statistics

Standard deviation63.466353
Coefficient of variation (CV)1.6891799
Kurtosis0.57106329
Mean37.572287
Median Absolute Deviation (MAD)0
Skewness1.4074056
Sum875810
Variance4027.9779
MonotonicityNot monotonic
2025-03-09T15:51:52.967727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15121
64.9%
145 3708
 
15.9%
1 140
 
0.6%
90 125
 
0.5%
2 102
 
0.4%
3 87
 
0.4%
4 86
 
0.4%
5 83
 
0.4%
6 79
 
0.3%
7 70
 
0.3%
Other values (273) 3709
 
15.9%
ValueCountFrequency (%)
0 15121
64.9%
1 140
 
0.6%
2 102
 
0.4%
3 87
 
0.4%
4 86
 
0.4%
5 83
 
0.4%
6 79
 
0.3%
7 70
 
0.3%
8 62
 
0.3%
9 67
 
0.3%
ValueCountFrequency (%)
282 1
 
< 0.1%
281 1
 
< 0.1%
280 1
 
< 0.1%
279 1
 
< 0.1%
278 1
 
< 0.1%
277 1
 
< 0.1%
276 2
< 0.1%
275 2
< 0.1%
274 4
< 0.1%
273 2
< 0.1%

BaseStylisme
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
23310 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
23310
100.0%

Length

2025-03-09T15:51:53.195901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:53.258177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

IDPatronnage
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
15516 
-1
7794 

Length

Max length2
Median length1
Mean length1.3343629
Min length1

Characters and Unicode

Total characters31104
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row0
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
0 15516
66.6%
-1 7794
33.4%

Length

2025-03-09T15:51:53.336841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:53.448864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15516
66.6%
1 7794
33.4%

Most occurring characters

ValueCountFrequency (%)
0 15516
49.9%
- 7794
25.1%
1 7794
25.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
74.9%
Dash Punctuation 7794
 
25.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15516
66.6%
1 7794
33.4%
Dash Punctuation
ValueCountFrequency (%)
- 7794
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31104
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15516
49.9%
- 7794
25.1%
1 7794
25.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31104
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15516
49.9%
- 7794
25.1%
1 7794
25.1%

IDTypeMatiereBase
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:53.592584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:53.701542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

IDVarianteModele
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:53.803443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:53.873917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

IDGenre
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:53.965329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:54.056277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

IDBroderie
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:54.170436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:54.246251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

IDSerigraphie
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:54.338389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:54.423186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

IDGarniture
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:54.519156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:54.603928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

IDTypeAccessoire
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:54.734155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:54.841990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

IDFournisseur
Real number (ℝ)

High correlation  Zeros 

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.201673
Minimum-1
Maximum709
Zeros15832
Zeros (%)67.9%
Negative31
Negative (%)0.1%
Memory size182.2 KiB
2025-03-09T15:51:54.980042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q3213
95-th percentile289
Maximum709
Range710
Interquartile range (IQR)213

Descriptive statistics

Standard deviation119.94938
Coefficient of variation (CV)1.5338467
Kurtosis0.29360253
Mean78.201673
Median Absolute Deviation (MAD)0
Skewness1.1696482
Sum1822881
Variance14387.853
MonotonicityNot monotonic
2025-03-09T15:51:55.154958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 15832
67.9%
213 5151
 
22.1%
289 1450
 
6.2%
440 434
 
1.9%
310 74
 
0.3%
283 62
 
0.3%
281 45
 
0.2%
48 37
 
0.2%
285 34
 
0.1%
-1 31
 
0.1%
Other values (19) 160
 
0.7%
ValueCountFrequency (%)
-1 31
 
0.1%
0 15832
67.9%
25 2
 
< 0.1%
32 8
 
< 0.1%
48 37
 
0.2%
64 3
 
< 0.1%
107 14
 
0.1%
213 5151
 
22.1%
278 3
 
< 0.1%
279 16
 
0.1%
ValueCountFrequency (%)
709 8
 
< 0.1%
676 5
 
< 0.1%
588 1
 
< 0.1%
523 9
 
< 0.1%
522 5
 
< 0.1%
440 434
1.9%
384 2
 
< 0.1%
360 13
 
0.1%
328 20
 
0.1%
311 15
 
0.1%

IDTransfert
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:55.338593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:55.422625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

IDCouleurGarniture
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:55.529474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:55.626893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

Observations
Text

Missing 

Distinct256
Distinct (%)1.6%
Missing6938
Missing (%)29.8%
Memory size1.1 MiB
2025-03-09T15:51:55.901717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length106
Median length7
Mean length7.1813462
Min length0

Characters and Unicode

Total characters117573
Distinct characters66
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique168 ?
Unique (%)1.0%

Sample

1st rowNAF NAF
2nd rowNAF NAF
3rd rowNAF NAF
4th rowNAF NAF
5th rowNAF NAF
ValueCountFrequency (%)
naf 30598
94.0%
base 216
 
0.7%
outlet 169
 
0.5%
sms 147
 
0.5%
pcs 125
 
0.4%
625 66
 
0.2%
boutique 47
 
0.1%
fin 39
 
0.1%
de 39
 
0.1%
stock 37
 
0.1%
Other values (326) 1084
 
3.3%
2025-03-09T15:51:56.560432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 31211
26.5%
N 30970
26.3%
F 30714
26.1%
16770
14.3%
S 829
 
0.7%
E 747
 
0.6%
T 562
 
0.5%
O 402
 
0.3%
L 351
 
0.3%
U 351
 
0.3%
Other values (56) 4666
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 98336
83.6%
Space Separator 16770
 
14.3%
Decimal Number 1427
 
1.2%
Lowercase Letter 957
 
0.8%
Other Punctuation 63
 
0.1%
Math Symbol 12
 
< 0.1%
Control 7
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 31211
31.7%
N 30970
31.5%
F 30714
31.2%
S 829
 
0.8%
E 747
 
0.8%
T 562
 
0.6%
O 402
 
0.4%
L 351
 
0.4%
U 351
 
0.4%
B 336
 
0.3%
Other values (16) 1863
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
e 157
16.4%
r 108
11.3%
u 98
10.2%
l 82
8.6%
o 78
8.2%
s 74
7.7%
n 69
7.2%
t 69
7.2%
a 66
6.9%
i 56
 
5.9%
Other values (12) 100
10.4%
Decimal Number
ValueCountFrequency (%)
1 266
18.6%
0 199
13.9%
3 185
13.0%
2 183
12.8%
5 157
11.0%
6 133
9.3%
9 84
 
5.9%
4 78
 
5.5%
8 78
 
5.5%
7 64
 
4.5%
Other Punctuation
ValueCountFrequency (%)
? 38
60.3%
% 22
34.9%
, 3
 
4.8%
Control
ValueCountFrequency (%)
4
57.1%
3
42.9%
Space Separator
ValueCountFrequency (%)
16770
100.0%
Math Symbol
ValueCountFrequency (%)
= 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 99293
84.5%
Common 18280
 
15.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 31211
31.4%
N 30970
31.2%
F 30714
30.9%
S 829
 
0.8%
E 747
 
0.8%
T 562
 
0.6%
O 402
 
0.4%
L 351
 
0.4%
U 351
 
0.4%
B 336
 
0.3%
Other values (38) 2820
 
2.8%
Common
ValueCountFrequency (%)
16770
91.7%
1 266
 
1.5%
0 199
 
1.1%
3 185
 
1.0%
2 183
 
1.0%
5 157
 
0.9%
6 133
 
0.7%
9 84
 
0.5%
4 78
 
0.4%
8 78
 
0.4%
Other values (8) 147
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 117573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 31211
26.5%
N 30970
26.3%
F 30714
26.1%
16770
14.3%
S 829
 
0.7%
E 747
 
0.6%
T 562
 
0.5%
O 402
 
0.3%
L 351
 
0.3%
U 351
 
0.3%
Other values (56) 4666
 
4.0%

IDCouleurBroderie
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:56.678261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:56.753771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

IDCouleurSerigraphie
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:56.834291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:56.909417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

PrixEmballage
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:51:57.005152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:57.100396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

StockMin
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:57.180615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:57.232449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

StockAlerte
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:57.289268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:57.336250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

AppliqueFodec
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
15515 
1
7795 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 15515
66.6%
1 7795
33.4%

Length

2025-03-09T15:51:57.391993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:57.440029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15515
66.6%
1 7795
33.4%

Most occurring characters

ValueCountFrequency (%)
0 15515
66.6%
1 7795
33.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15515
66.6%
1 7795
33.4%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15515
66.6%
1 7795
33.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15515
66.6%
1 7795
33.4%

ValeurMPEuro
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:51:57.515376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:57.603627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

ValeurMPAutre
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:51:57.705370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:57.789574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

TypeArticleService
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
21614 
255
 
1647
1
 
49

Length

Max length3
Median length1
Mean length1.1413127
Min length1

Characters and Unicode

Total characters26604
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21614
92.7%
255 1647
 
7.1%
1 49
 
0.2%

Length

2025-03-09T15:51:57.874298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:57.923529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 21614
92.7%
255 1647
 
7.1%
1 49
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 21614
81.2%
5 3294
 
12.4%
2 1647
 
6.2%
1 49
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26604
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21614
81.2%
5 3294
 
12.4%
2 1647
 
6.2%
1 49
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 26604
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21614
81.2%
5 3294
 
12.4%
2 1647
 
6.2%
1 49
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26604
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21614
81.2%
5 3294
 
12.4%
2 1647
 
6.2%
1 49
 
0.2%

ValeurMPTunisie
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:51:57.989101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:58.020483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

ValeurMPEuromed
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:51:58.068822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:58.115700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

AQL
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:51:58.174791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:58.207228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

AQLMineur
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:51:58.254100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:58.308224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

IDNiveauControle
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:58.358340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:58.399543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

AQLCritique
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:51:58.447417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:58.496988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

IDCategorie
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:58.599430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:58.691741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

DateValidationNomenclature
Date

Missing 

Distinct3
Distinct (%)100.0%
Missing23307
Missing (%)> 99.9%
Memory size728.6 KiB
Minimum2024-05-03 00:00:00
Maximum2024-08-12 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-09T15:51:58.776504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:58.870310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

NomenclatureValidePar
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
23307 
superviseur
 
2
benoit.dupuy
 
1

Length

Max length12
Median length0
Mean length0.0014586015
Min length0

Characters and Unicode

Total characters34
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
23307
> 99.9%
superviseur 2
 
< 0.1%
benoit.dupuy 1
 
< 0.1%

Length

2025-03-09T15:51:58.952698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:59.000684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
superviseur 2
66.7%
benoit.dupuy 1
33.3%

Most occurring characters

ValueCountFrequency (%)
u 6
17.6%
e 5
14.7%
s 4
11.8%
r 4
11.8%
p 3
8.8%
i 3
8.8%
v 2
 
5.9%
b 1
 
2.9%
n 1
 
2.9%
o 1
 
2.9%
Other values (4) 4
11.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 33
97.1%
Other Punctuation 1
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 6
18.2%
e 5
15.2%
s 4
12.1%
r 4
12.1%
p 3
9.1%
i 3
9.1%
v 2
 
6.1%
b 1
 
3.0%
n 1
 
3.0%
o 1
 
3.0%
Other values (3) 3
9.1%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 33
97.1%
Common 1
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 6
18.2%
e 5
15.2%
s 4
12.1%
r 4
12.1%
p 3
9.1%
i 3
9.1%
v 2
 
6.1%
b 1
 
3.0%
n 1
 
3.0%
o 1
 
3.0%
Other values (3) 3
9.1%
Common
ValueCountFrequency (%)
. 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 6
17.6%
e 5
14.7%
s 4
11.8%
r 4
11.8%
p 3
8.8%
i 3
8.8%
v 2
 
5.9%
b 1
 
2.9%
n 1
 
2.9%
o 1
 
2.9%
Other values (4) 4
11.8%

IDCategoriereclamation
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:59.063364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:59.369639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

IDCartouche
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:59.431408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:59.476660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

IDArticleParent
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:59.535267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:59.578323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

isParent
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:59.632271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:59.679281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

QteFils
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:51:59.743868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:59.791527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

NbrPiecesColis
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23304 
2
 
4
2484
 
1
1
 
1

Length

Max length4
Median length1
Mean length1.0001287
Min length1

Characters and Unicode

Total characters23313
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23304
> 99.9%
2 4
 
< 0.1%
2484 1
 
< 0.1%
1 1
 
< 0.1%

Length

2025-03-09T15:51:59.850422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:51:59.912549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23304
> 99.9%
2 4
 
< 0.1%
2484 1
 
< 0.1%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 23304
> 99.9%
2 5
 
< 0.1%
4 2
 
< 0.1%
8 1
 
< 0.1%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23313
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23304
> 99.9%
2 5
 
< 0.1%
4 2
 
< 0.1%
8 1
 
< 0.1%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 23313
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23304
> 99.9%
2 5
 
< 0.1%
4 2
 
< 0.1%
8 1
 
< 0.1%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23304
> 99.9%
2 5
 
< 0.1%
4 2
 
< 0.1%
8 1
 
< 0.1%
1 1
 
< 0.1%

NbrColisPalette
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23308 
1
 
1
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23308
> 99.9%
1 1
 
< 0.1%
4 1
 
< 0.1%

Length

2025-03-09T15:51:59.962343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:00.023818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23308
> 99.9%
1 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 23308
> 99.9%
1 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23308
> 99.9%
1 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23308
> 99.9%
1 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23308
> 99.9%
1 1
 
< 0.1%
4 1
 
< 0.1%

Dimensions
Unsupported

Missing  Rejected  Unsupported 

Missing23310
Missing (%)100.0%
Memory size182.2 KiB

TempsAtelier
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:52:00.089719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:00.124727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

TempsFinitions
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:52:00.179440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:00.220553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

ArticleLong
Text

Missing 

Distinct14140
Distinct (%)76.8%
Missing4892
Missing (%)21.0%
Memory size1.2 MiB
2025-03-09T15:52:00.495868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length179
Median length132
Mean length9.5655337
Min length0

Characters and Unicode

Total characters176178
Distinct characters80
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12466 ?
Unique (%)67.7%

Sample

1st rowESHONIE SH1B
2nd rowEDORI SH1
3rd rowEBANE SH1 B
4th rowFARIS SH1
5th rowFARRAN SH2
ValueCountFrequency (%)
r1 2228
 
6.3%
c1 1374
 
3.9%
j1 945
 
2.7%
p1 925
 
2.6%
bo 894
 
2.5%
ml 809
 
2.3%
mc 804
 
2.3%
t1 579
 
1.6%
sm 456
 
1.3%
d1 348
 
1.0%
Other values (9492) 25982
73.5%
2025-03-09T15:52:01.287912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17385
 
9.9%
A 15804
 
9.0%
E 14819
 
8.4%
R 10913
 
6.2%
L 10873
 
6.2%
I 10487
 
6.0%
O 10101
 
5.7%
N 8168
 
4.6%
1 7702
 
4.4%
M 7310
 
4.1%
Other values (70) 62616
35.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 144215
81.9%
Space Separator 17385
 
9.9%
Decimal Number 8725
 
5.0%
Lowercase Letter 4830
 
2.7%
Dash Punctuation 638
 
0.4%
Other Punctuation 351
 
0.2%
Control 32
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 15804
 
11.0%
E 14819
 
10.3%
R 10913
 
7.6%
L 10873
 
7.5%
I 10487
 
7.3%
O 10101
 
7.0%
N 8168
 
5.7%
M 7310
 
5.1%
C 7180
 
5.0%
S 6842
 
4.7%
Other values (20) 41718
28.9%
Lowercase Letter
ValueCountFrequency (%)
e 778
16.1%
a 447
 
9.3%
s 375
 
7.8%
o 332
 
6.9%
l 327
 
6.8%
n 326
 
6.7%
t 286
 
5.9%
c 272
 
5.6%
u 261
 
5.4%
r 254
 
5.3%
Other values (18) 1172
24.3%
Decimal Number
ValueCountFrequency (%)
1 7702
88.3%
2 757
 
8.7%
3 117
 
1.3%
8 29
 
0.3%
7 28
 
0.3%
4 27
 
0.3%
0 24
 
0.3%
5 24
 
0.3%
6 15
 
0.2%
9 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 153
43.6%
? 111
31.6%
. 35
 
10.0%
, 26
 
7.4%
& 16
 
4.6%
' 5
 
1.4%
% 5
 
1.4%
Control
ValueCountFrequency (%)
16
50.0%
16
50.0%
Space Separator
ValueCountFrequency (%)
17385
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 638
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 149045
84.6%
Common 27133
 
15.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15804
 
10.6%
E 14819
 
9.9%
R 10913
 
7.3%
L 10873
 
7.3%
I 10487
 
7.0%
O 10101
 
6.8%
N 8168
 
5.5%
M 7310
 
4.9%
C 7180
 
4.8%
S 6842
 
4.6%
Other values (48) 46548
31.2%
Common
ValueCountFrequency (%)
17385
64.1%
1 7702
28.4%
2 757
 
2.8%
- 638
 
2.4%
/ 153
 
0.6%
3 117
 
0.4%
? 111
 
0.4%
. 35
 
0.1%
8 29
 
0.1%
7 28
 
0.1%
Other values (12) 178
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176135
> 99.9%
None 43
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17385
 
9.9%
A 15804
 
9.0%
E 14819
 
8.4%
R 10913
 
6.2%
L 10873
 
6.2%
I 10487
 
6.0%
O 10101
 
5.7%
N 8168
 
4.6%
1 7702
 
4.4%
M 7310
 
4.2%
Other values (64) 62573
35.5%
None
ValueCountFrequency (%)
é 17
39.5%
É 8
18.6%
à 7
16.3%
Π5
 
11.6%
Ä 4
 
9.3%
À 2
 
4.7%

IDTypeMatelassage
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:52:01.412797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:01.453038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

IDMP
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:52:01.508801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:01.549921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

IsMP
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:52:01.597947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:01.646170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

IDDecorArticle
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:52:01.705721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:01.747683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

IsSemiFini
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:52:01.797026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:01.839181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

PoidsEmballage
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23264 
1.0
 
46

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23264
99.8%
1.0 46
 
0.2%

Length

2025-03-09T15:52:01.888907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:01.921558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23264
99.8%
1.0 46
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 46574
66.6%
. 23310
33.3%
1 46
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46574
99.9%
1 46
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46574
66.6%
. 23310
33.3%
1 46
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46574
66.6%
. 23310
33.3%
1 46
 
0.1%

TempsUnitaire
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:52:01.973072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:02.031029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

IDcomplexite
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
15516 
-1
7794 

Length

Max length2
Median length1
Mean length1.3343629
Min length1

Characters and Unicode

Total characters31104
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row0
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
0 15516
66.6%
-1 7794
33.4%

Length

2025-03-09T15:52:02.077544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:02.140889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15516
66.6%
1 7794
33.4%

Most occurring characters

ValueCountFrequency (%)
0 15516
49.9%
- 7794
25.1%
1 7794
25.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
74.9%
Dash Punctuation 7794
 
25.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15516
66.6%
1 7794
33.4%
Dash Punctuation
ValueCountFrequency (%)
- 7794
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31104
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15516
49.9%
- 7794
25.1%
1 7794
25.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31104
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15516
49.9%
- 7794
25.1%
1 7794
25.1%

TauxSondageQlte
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:52:02.199956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:02.241174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

IDNorme
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:52:02.353849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:02.438404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

IDAr_Theme
Real number (ℝ)

High correlation  Zeros 

Distinct44
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1402402
Minimum-1
Maximum74
Zeros21299
Zeros (%)91.4%
Negative253
Negative (%)1.1%
Memory size182.2 KiB
2025-03-09T15:52:02.562615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q30
95-th percentile51
Maximum74
Range75
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.197478
Coefficient of variation (CV)3.6706754
Kurtosis11.057152
Mean4.1402402
Median Absolute Deviation (MAD)0
Skewness3.5510871
Sum96509
Variance230.96334
MonotonicityNot monotonic
2025-03-09T15:52:02.709433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 21299
91.4%
-1 253
 
1.1%
51 225
 
1.0%
27 120
 
0.5%
69 119
 
0.5%
62 107
 
0.5%
71 99
 
0.4%
61 97
 
0.4%
50 88
 
0.4%
59 81
 
0.3%
Other values (34) 822
 
3.5%
ValueCountFrequency (%)
-1 253
 
1.1%
0 21299
91.4%
1 2
 
< 0.1%
3 7
 
< 0.1%
4 13
 
0.1%
9 45
 
0.2%
24 24
 
0.1%
25 10
 
< 0.1%
26 3
 
< 0.1%
27 120
 
0.5%
ValueCountFrequency (%)
74 46
 
0.2%
73 46
 
0.2%
72 66
0.3%
71 99
0.4%
70 51
0.2%
69 119
0.5%
68 38
 
0.2%
67 53
0.2%
66 30
 
0.1%
65 3
 
< 0.1%

PrixAchat
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct1237
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.274819
Minimum0
Maximum571428.57
Zeros2297
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size182.2 KiB
2025-03-09T15:52:02.823776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.56
median8.57
Q314
95-th percentile24.57
Maximum571428.57
Range571428.57
Interquartile range (IQR)9.44

Descriptive statistics

Standard deviation5430.3908
Coefficient of variation (CV)80.719515
Kurtosis10573.413
Mean67.274819
Median Absolute Deviation (MAD)4.335
Skewness101.65777
Sum1568176
Variance29489144
MonotonicityNot monotonic
2025-03-09T15:52:02.958173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2297
 
9.9%
14.28 862
 
3.7%
11.43 816
 
3.5%
8.57 612
 
2.6%
2.83 607
 
2.6%
12.85 596
 
2.6%
17.14 523
 
2.2%
10 518
 
2.2%
20 475
 
2.0%
5.71 474
 
2.0%
Other values (1227) 15530
66.6%
ValueCountFrequency (%)
0 2297
9.9%
0.01 105
 
0.5%
0.03 2
 
< 0.1%
0.1 5
 
< 0.1%
0.228 1
 
< 0.1%
0.29 414
 
1.8%
0.54 1
 
< 0.1%
0.57 1
 
< 0.1%
0.83 1
 
< 0.1%
0.836 1
 
< 0.1%
ValueCountFrequency (%)
571428.57 2
< 0.1%
185478.55 1
 
< 0.1%
143.7 1
 
< 0.1%
133.95 1
 
< 0.1%
120.43 1
 
< 0.1%
115.55 3
< 0.1%
110.28 1
 
< 0.1%
110.1 1
 
< 0.1%
98.7 3
< 0.1%
94.2 2
< 0.1%

Emballage
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
23309 
POLYBAG
 
1

Length

Max length7
Median length0
Mean length0.0003003003
Min length0

Characters and Unicode

Total characters7
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
23309
> 99.9%
POLYBAG 1
 
< 0.1%

Length

2025-03-09T15:52:03.065559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:03.118558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
polybag 1
100.0%

Most occurring characters

ValueCountFrequency (%)
P 1
14.3%
O 1
14.3%
L 1
14.3%
Y 1
14.3%
B 1
14.3%
A 1
14.3%
G 1
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 1
14.3%
O 1
14.3%
L 1
14.3%
Y 1
14.3%
B 1
14.3%
A 1
14.3%
G 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 7
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 1
14.3%
O 1
14.3%
L 1
14.3%
Y 1
14.3%
B 1
14.3%
A 1
14.3%
G 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 1
14.3%
O 1
14.3%
L 1
14.3%
Y 1
14.3%
B 1
14.3%
A 1
14.3%
G 1
14.3%

Boutonnage
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
23309 
Chemise-Chemisier
 
1

Length

Max length17
Median length0
Mean length0.00072930073
Min length0

Characters and Unicode

Total characters17
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
23309
> 99.9%
Chemise-Chemisier 1
 
< 0.1%

Length

2025-03-09T15:52:03.186666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:03.241607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
chemise-chemisier 1
100.0%

Most occurring characters

ValueCountFrequency (%)
e 4
23.5%
i 3
17.6%
h 2
11.8%
C 2
11.8%
m 2
11.8%
s 2
11.8%
- 1
 
5.9%
r 1
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14
82.4%
Uppercase Letter 2
 
11.8%
Dash Punctuation 1
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4
28.6%
i 3
21.4%
h 2
14.3%
m 2
14.3%
s 2
14.3%
r 1
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
C 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16
94.1%
Common 1
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4
25.0%
i 3
18.8%
h 2
12.5%
C 2
12.5%
m 2
12.5%
s 2
12.5%
r 1
 
6.2%
Common
ValueCountFrequency (%)
- 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 4
23.5%
i 3
17.6%
h 2
11.8%
C 2
11.8%
m 2
11.8%
s 2
11.8%
- 1
 
5.9%
r 1
 
5.9%

PieceCarton
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
15515 
255
7067 
1
 
597
2
 
131

Length

Max length3
Median length1
Mean length1.6063492
Min length1

Characters and Unicode

Total characters37444
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row255
2nd row0
3rd row255
4th row255
5th row255

Common Values

ValueCountFrequency (%)
0 15515
66.6%
255 7067
30.3%
1 597
 
2.6%
2 131
 
0.6%

Length

2025-03-09T15:52:03.312687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:03.386086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15515
66.6%
255 7067
30.3%
1 597
 
2.6%
2 131
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 15515
41.4%
5 14134
37.7%
2 7198
19.2%
1 597
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37444
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15515
41.4%
5 14134
37.7%
2 7198
19.2%
1 597
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 37444
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15515
41.4%
5 14134
37.7%
2 7198
19.2%
1 597
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37444
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15515
41.4%
5 14134
37.7%
2 7198
19.2%
1 597
 
1.6%

SupportArt
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
23309 
PAP Prêt à porter
 
1

Length

Max length17
Median length0
Mean length0.00072930073
Min length0

Characters and Unicode

Total characters17
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
23309
> 99.9%
PAP Prêt à porter 1
 
< 0.1%

Length

2025-03-09T15:52:03.460906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:03.505159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pap 1
25.0%
prêt 1
25.0%
à 1
25.0%
porter 1
25.0%

Most occurring characters

ValueCountFrequency (%)
P 3
17.6%
3
17.6%
r 3
17.6%
t 2
11.8%
A 1
 
5.9%
ê 1
 
5.9%
à 1
 
5.9%
p 1
 
5.9%
o 1
 
5.9%
e 1
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10
58.8%
Uppercase Letter 4
 
23.5%
Space Separator 3
 
17.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 3
30.0%
t 2
20.0%
ê 1
 
10.0%
à 1
 
10.0%
p 1
 
10.0%
o 1
 
10.0%
e 1
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
P 3
75.0%
A 1
 
25.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14
82.4%
Common 3
 
17.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 3
21.4%
r 3
21.4%
t 2
14.3%
A 1
 
7.1%
ê 1
 
7.1%
à 1
 
7.1%
p 1
 
7.1%
o 1
 
7.1%
e 1
 
7.1%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
88.2%
None 2
 
11.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 3
20.0%
3
20.0%
r 3
20.0%
t 2
13.3%
A 1
 
6.7%
p 1
 
6.7%
o 1
 
6.7%
e 1
 
6.7%
None
ValueCountFrequency (%)
ê 1
50.0%
à 1
50.0%

IDPays
Real number (ℝ)

High correlation  Zeros 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.623895
Minimum-1
Maximum207
Zeros431
Zeros (%)1.8%
Negative730
Negative (%)3.1%
Memory size182.2 KiB
2025-03-09T15:52:03.566265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q127
median103
Q3172
95-th percentile176
Maximum207
Range208
Interquartile range (IQR)145

Descriptive statistics

Standard deviation71.535919
Coefficient of variation (CV)0.71805985
Kurtosis-1.5768081
Mean99.623895
Median Absolute Deviation (MAD)69
Skewness-0.28969445
Sum2322233
Variance5117.3877
MonotonicityNot monotonic
2025-03-09T15:52:03.661236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
172 4491
19.3%
1 4393
18.8%
103 3498
15.0%
176 2398
10.3%
35 2080
8.9%
171 1538
 
6.6%
165 1256
 
5.4%
107 1006
 
4.3%
-1 730
 
3.1%
99 522
 
2.2%
Other values (16) 1398
 
6.0%
ValueCountFrequency (%)
-1 730
 
3.1%
0 431
 
1.8%
1 4393
18.8%
2 1
 
< 0.1%
13 78
 
0.3%
18 178
 
0.8%
27 18
 
0.1%
28 418
 
1.8%
29 3
 
< 0.1%
35 2080
8.9%
ValueCountFrequency (%)
207 9
 
< 0.1%
177 17
 
0.1%
176 2398
10.3%
174 29
 
0.1%
173 177
 
0.8%
172 4491
19.3%
171 1538
 
6.6%
165 1256
 
5.4%
152 8
 
< 0.1%
128 4
 
< 0.1%

ReseauArt
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
23309 
7
 
1

Length

Max length1
Median length0
Mean length4.2900043 × 10-5
Min length0

Characters and Unicode

Total characters1
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
23309
> 99.9%
7 1
 
< 0.1%

Length

2025-03-09T15:52:03.736824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:03.787090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
7 1
100.0%

Most occurring characters

ValueCountFrequency (%)
7 1
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 1
100.0%

ReferenceFssr
Text

Distinct175
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2025-03-09T15:52:03.916807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length43
Median length0
Mean length0.14272844
Min length0

Characters and Unicode

Total characters3327
Distinct characters42
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)0.3%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
p24-108 12
 
2.1%
model 11
 
1.9%
421 10
 
1.8%
t1 10
 
1.8%
2003 9
 
1.6%
058 9
 
1.6%
5102 9
 
1.6%
flore 9
 
1.6%
5109 9
 
1.6%
840 9
 
1.6%
Other values (194) 471
82.9%
2025-03-09T15:52:04.266504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 338
 
10.2%
1 283
 
8.5%
2 278
 
8.4%
3 194
 
5.8%
4 181
 
5.4%
5 177
 
5.3%
- 170
 
5.1%
8 130
 
3.9%
9 129
 
3.9%
7 128
 
3.8%
Other values (32) 1319
39.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1959
58.9%
Uppercase Letter 1107
33.3%
Dash Punctuation 170
 
5.1%
Space Separator 81
 
2.4%
Other Punctuation 10
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 91
 
8.2%
S 89
 
8.0%
E 88
 
7.9%
P 83
 
7.5%
A 82
 
7.4%
F 67
 
6.1%
T 63
 
5.7%
B 60
 
5.4%
L 59
 
5.3%
R 54
 
4.9%
Other values (19) 371
33.5%
Decimal Number
ValueCountFrequency (%)
0 338
17.3%
1 283
14.4%
2 278
14.2%
3 194
9.9%
4 181
9.2%
5 177
9.0%
8 130
 
6.6%
9 129
 
6.6%
7 128
 
6.5%
6 121
 
6.2%
Dash Punctuation
ValueCountFrequency (%)
- 170
100.0%
Space Separator
ValueCountFrequency (%)
81
100.0%
Other Punctuation
ValueCountFrequency (%)
. 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2220
66.7%
Latin 1107
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 91
 
8.2%
S 89
 
8.0%
E 88
 
7.9%
P 83
 
7.5%
A 82
 
7.4%
F 67
 
6.1%
T 63
 
5.7%
B 60
 
5.4%
L 59
 
5.3%
R 54
 
4.9%
Other values (19) 371
33.5%
Common
ValueCountFrequency (%)
0 338
15.2%
1 283
12.7%
2 278
12.5%
3 194
8.7%
4 181
8.2%
5 177
8.0%
- 170
7.7%
8 130
 
5.9%
9 129
 
5.8%
7 128
 
5.8%
Other values (3) 212
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3317
99.7%
None 10
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 338
 
10.2%
1 283
 
8.5%
2 278
 
8.4%
3 194
 
5.8%
4 181
 
5.5%
5 177
 
5.3%
- 170
 
5.1%
8 130
 
3.9%
9 129
 
3.9%
7 128
 
3.9%
Other values (29) 1309
39.5%
None
ValueCountFrequency (%)
Ü 6
60.0%
Ö 3
30.0%
Ç 1
 
10.0%

DDV
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
23310 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
23310
100.0%

Length

2025-03-09T15:52:04.398361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:04.477497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FraisTransport
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:52:04.577309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:04.668355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

AutresFrais
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:52:04.774071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:04.873475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

IDFibreComposition
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23286 
-1
 
15
73
 
9

Length

Max length2
Median length1
Mean length1.0010296
Min length1

Characters and Unicode

Total characters23334
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
0 23286
99.9%
-1 15
 
0.1%
73 9
 
< 0.1%

Length

2025-03-09T15:52:04.973731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:05.034715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23286
99.9%
1 15
 
0.1%
73 9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 23286
99.8%
- 15
 
0.1%
1 15
 
0.1%
7 9
 
< 0.1%
3 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23319
99.9%
Dash Punctuation 15
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23286
99.9%
1 15
 
0.1%
7 9
 
< 0.1%
3 9
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23334
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23286
99.8%
- 15
 
0.1%
1 15
 
0.1%
7 9
 
< 0.1%
3 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23334
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23286
99.8%
- 15
 
0.1%
1 15
 
0.1%
7 9
 
< 0.1%
3 9
 
< 0.1%

IDArticleEtqEntretien
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:52:05.101386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:05.134660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

DateMEP
Date

Missing 

Distinct108
Distinct (%)2.3%
Missing18629
Missing (%)79.9%
Memory size765.1 KiB
Minimum2020-04-15 00:00:00
Maximum2025-11-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-09T15:52:05.202656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:52:05.307299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

IDUsine
Real number (ℝ)

High correlation  Zeros 

Distinct68
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.247104
Minimum0
Maximum225
Zeros16505
Zeros (%)70.8%
Negative0
Negative (%)0.0%
Memory size182.2 KiB
2025-03-09T15:52:05.427971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q359
95-th percentile172
Maximum225
Range225
Interquartile range (IQR)59

Descriptive statistics

Standard deviation55.180397
Coefficient of variation (CV)1.82432
Kurtosis2.4613866
Mean30.247104
Median Absolute Deviation (MAD)0
Skewness1.8493919
Sum705060
Variance3044.8762
MonotonicityNot monotonic
2025-03-09T15:52:05.641046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16505
70.8%
59 3168
 
13.6%
97 1138
 
4.9%
202 439
 
1.9%
172 313
 
1.3%
136 242
 
1.0%
158 209
 
0.9%
98 110
 
0.5%
212 100
 
0.4%
131 97
 
0.4%
Other values (58) 989
 
4.2%
ValueCountFrequency (%)
0 16505
70.8%
2 3
 
< 0.1%
5 25
 
0.1%
11 4
 
< 0.1%
35 36
 
0.2%
41 2
 
< 0.1%
59 3168
 
13.6%
97 1138
 
4.9%
98 110
 
0.5%
122 15
 
0.1%
ValueCountFrequency (%)
225 8
 
< 0.1%
224 4
 
< 0.1%
223 10
 
< 0.1%
222 6
 
< 0.1%
221 8
 
< 0.1%
220 17
 
0.1%
214 19
 
0.1%
213 6
 
< 0.1%
212 100
0.4%
211 8
 
< 0.1%

IDSupportArticle
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.080652081
Minimum0
Maximum6
Zeros22366
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size182.2 KiB
2025-03-09T15:52:05.786155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.50253013
Coefficient of variation (CV)6.230839
Kurtosis86.788813
Mean0.080652081
Median Absolute Deviation (MAD)0
Skewness8.7021756
Sum1880
Variance0.25253653
MonotonicityNot monotonic
2025-03-09T15:52:05.906792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 22366
96.0%
1 511
 
2.2%
2 287
 
1.2%
6 71
 
0.3%
5 70
 
0.3%
4 4
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
0 22366
96.0%
1 511
 
2.2%
2 287
 
1.2%
3 1
 
< 0.1%
4 4
 
< 0.1%
5 70
 
0.3%
6 71
 
0.3%
ValueCountFrequency (%)
6 71
 
0.3%
5 70
 
0.3%
4 4
 
< 0.1%
3 1
 
< 0.1%
2 287
 
1.2%
1 511
 
2.2%
0 22366
96.0%

Ecologique
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:52:06.010462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:06.064224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

TauxDefectueux
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:52:06.336364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:06.387592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Publier
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:52:06.442167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:06.490183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

CompositionMatiere
Text

Missing 

Distinct362
Distinct (%)23.1%
Missing21743
Missing (%)93.3%
Memory size812.6 KiB
2025-03-09T15:52:06.684040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length93
Median length84
Mean length29.919592
Min length0

Characters and Unicode

Total characters46884
Distinct characters60
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique202 ?
Unique (%)12.9%

Sample

1st row48.25% Acier 59.75% Acrylique
2nd row
3rd row-0.95% POLYESTER -0.05% ELASTHANNE
4th row70% Polyester 29.1% Viscose 0.9% Elasthanne
5th row70% Polyester 29.1% Viscose 0.9% Elasthanne
ValueCountFrequency (%)
polyester 1063
16.3%
viscose 743
 
11.4%
elasthanne 504
 
7.7%
100 370
 
5.7%
0 338
 
5.2%
30 297
 
4.6%
coton 289
 
4.4%
polyamide 232
 
3.6%
70 223
 
3.4%
5 130
 
2.0%
Other values (245) 2337
35.8%
2025-03-09T15:52:07.018085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9752
20.8%
e 3878
 
8.3%
% 3220
 
6.9%
s 3019
 
6.4%
o 2616
 
5.6%
0 2082
 
4.4%
t 2079
 
4.4%
l 2027
 
4.3%
a 1452
 
3.1%
y 1359
 
2.9%
Other values (50) 15400
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22685
48.4%
Space Separator 9752
20.8%
Decimal Number 6490
 
13.8%
Uppercase Letter 4093
 
8.7%
Other Punctuation 3805
 
8.1%
Dash Punctuation 59
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 1332
32.5%
V 743
18.2%
E 650
15.9%
C 326
 
8.0%
A 244
 
6.0%
L 139
 
3.4%
S 133
 
3.2%
O 85
 
2.1%
F 73
 
1.8%
T 71
 
1.7%
Other values (13) 297
 
7.3%
Lowercase Letter
ValueCountFrequency (%)
e 3878
17.1%
s 3019
13.3%
o 2616
11.5%
t 2079
9.2%
l 2027
8.9%
a 1452
 
6.4%
y 1359
 
6.0%
n 1325
 
5.8%
r 1235
 
5.4%
i 1220
 
5.4%
Other values (12) 2475
10.9%
Decimal Number
ValueCountFrequency (%)
0 2082
32.1%
1 768
 
11.8%
5 671
 
10.3%
3 597
 
9.2%
7 518
 
8.0%
2 465
 
7.2%
8 381
 
5.9%
4 350
 
5.4%
6 340
 
5.2%
9 318
 
4.9%
Other Punctuation
ValueCountFrequency (%)
% 3220
84.6%
. 584
 
15.3%
' 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
9752
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 59
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26778
57.1%
Common 20106
42.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3878
14.5%
s 3019
11.3%
o 2616
9.8%
t 2079
 
7.8%
l 2027
 
7.6%
a 1452
 
5.4%
y 1359
 
5.1%
P 1332
 
5.0%
n 1325
 
4.9%
r 1235
 
4.6%
Other values (35) 6456
24.1%
Common
ValueCountFrequency (%)
9752
48.5%
% 3220
 
16.0%
0 2082
 
10.4%
1 768
 
3.8%
5 671
 
3.3%
3 597
 
3.0%
. 584
 
2.9%
7 518
 
2.6%
2 465
 
2.3%
8 381
 
1.9%
Other values (5) 1068
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46617
99.4%
None 267
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9752
20.9%
e 3878
 
8.3%
% 3220
 
6.9%
s 3019
 
6.5%
o 2616
 
5.6%
0 2082
 
4.5%
t 2079
 
4.5%
l 2027
 
4.3%
a 1452
 
3.1%
y 1359
 
2.9%
Other values (47) 15133
32.5%
None
ValueCountFrequency (%)
é 257
96.3%
É 9
 
3.4%
è 1
 
0.4%

IDAr_Looks
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2007293
Minimum0
Maximum9
Zeros22537
Zeros (%)96.7%
Negative0
Negative (%)0.0%
Memory size182.2 KiB
2025-03-09T15:52:07.081487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.18389
Coefficient of variation (CV)5.8979429
Kurtosis36.849397
Mean0.2007293
Median Absolute Deviation (MAD)0
Skewness6.1483792
Sum4679
Variance1.4015954
MonotonicityNot monotonic
2025-03-09T15:52:07.147600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 22537
96.7%
8 479
 
2.1%
3 116
 
0.5%
2 106
 
0.5%
6 29
 
0.1%
1 22
 
0.1%
4 18
 
0.1%
5 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 22537
96.7%
1 22
 
0.1%
2 106
 
0.5%
3 116
 
0.5%
4 18
 
0.1%
5 2
 
< 0.1%
6 29
 
0.1%
8 479
 
2.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 479
 
2.1%
6 29
 
0.1%
5 2
 
< 0.1%
4 18
 
0.1%
3 116
 
0.5%
2 106
 
0.5%
1 22
 
0.1%
0 22537
96.7%

PrixOutlet
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23284 
20.0
 
19
1.0
 
5
-1.0
 
1
10.0
 
1

Length

Max length4
Median length3
Mean length3.0009009
Min length3

Characters and Unicode

Total characters69951
Distinct characters5
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23284
99.9%
20.0 19
 
0.1%
1.0 5
 
< 0.1%
-1.0 1
 
< 0.1%
10.0 1
 
< 0.1%

Length

2025-03-09T15:52:07.223884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:07.289052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23284
99.9%
20.0 19
 
0.1%
1.0 6
 
< 0.1%
10.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 46614
66.6%
. 23310
33.3%
2 19
 
< 0.1%
1 7
 
< 0.1%
- 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46640
66.7%
Other Punctuation 23310
33.3%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46614
99.9%
2 19
 
< 0.1%
1 7
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69951
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46614
66.6%
. 23310
33.3%
2 19
 
< 0.1%
1 7
 
< 0.1%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69951
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46614
66.6%
. 23310
33.3%
2 19
 
< 0.1%
1 7
 
< 0.1%
- 1
 
< 0.1%

Matiere
Categorical

High correlation  Missing 

Distinct17
Distinct (%)25.8%
Missing23244
Missing (%)99.7%
Memory size1.2 MiB
MAILLE
15 
COTON
7 
JERSEY
6 
Crêpe Marocain
6 
Satin
4 
Other values (12)
28 

Length

Max length14
Median length13
Mean length6.6969697
Min length0

Characters and Unicode

Total characters442
Distinct characters33
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)3.0%

Sample

1st rowCrêpe
2nd rowCrêpe Marocain
3rd rowCrêpe Marocain
4th rowCrêpe Marocain
5th rowJersey

Common Values

ValueCountFrequency (%)
MAILLE 15
 
0.1%
COTON 7
 
< 0.1%
JERSEY 6
 
< 0.1%
Crêpe Marocain 6
 
< 0.1%
Satin 4
 
< 0.1%
DEVEAUX 4
 
< 0.1%
PU 3
 
< 0.1%
Denim 3
 
< 0.1%
Jersey 3
 
< 0.1%
VISCOSE 3
 
< 0.1%
Other values (7) 12
 
0.1%
(Missing) 23244
99.7%

Length

2025-03-09T15:52:07.403948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maille 15
20.0%
coton 11
14.7%
jersey 9
12.0%
crêpe 8
10.7%
marocain 6
 
8.0%
satin 6
 
8.0%
deveaux 4
 
5.3%
denim 4
 
5.3%
pu 3
 
4.0%
viscose 3
 
4.0%
Other values (3) 6
 
8.0%

Most occurring characters

ValueCountFrequency (%)
E 47
 
10.6%
L 34
 
7.7%
O 27
 
6.1%
C 24
 
5.4%
I 23
 
5.2%
M 22
 
5.0%
A 21
 
4.8%
S 20
 
4.5%
T 17
 
3.8%
e 17
 
3.8%
Other values (23) 190
43.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 315
71.3%
Lowercase Letter 117
 
26.5%
Space Separator 10
 
2.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 47
14.9%
L 34
10.8%
O 27
8.6%
C 24
 
7.6%
I 23
 
7.3%
M 22
 
7.0%
A 21
 
6.7%
S 20
 
6.3%
T 17
 
5.4%
N 16
 
5.1%
Other values (9) 64
20.3%
Lowercase Letter
ValueCountFrequency (%)
e 17
14.5%
r 17
14.5%
a 16
13.7%
i 13
11.1%
n 13
11.1%
ê 8
6.8%
p 8
6.8%
o 6
 
5.1%
c 6
 
5.1%
t 4
 
3.4%
Other values (3) 9
7.7%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 432
97.7%
Common 10
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 47
 
10.9%
L 34
 
7.9%
O 27
 
6.2%
C 24
 
5.6%
I 23
 
5.3%
M 22
 
5.1%
A 21
 
4.9%
S 20
 
4.6%
T 17
 
3.9%
e 17
 
3.9%
Other values (22) 180
41.7%
Common
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 434
98.2%
None 8
 
1.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 47
 
10.8%
L 34
 
7.8%
O 27
 
6.2%
C 24
 
5.5%
I 23
 
5.3%
M 22
 
5.1%
A 21
 
4.8%
S 20
 
4.6%
T 17
 
3.9%
e 17
 
3.9%
Other values (22) 182
41.9%
None
ValueCountFrequency (%)
ê 8
100.0%

Ordre
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
23310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23310
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23310
100.0%

Length

2025-03-09T15:52:07.499844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:07.531157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23310
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23310
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23310
100.0%

TauxCommissionCA
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:52:07.595794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:07.642669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

CODE_OLD
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
23310 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
23310
100.0%

Length

2025-03-09T15:52:07.690855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:07.739783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

IDPlanComptable
Real number (ℝ)

High correlation  Zeros 

Distinct90
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0023166
Minimum-1
Maximum1401
Zeros18078
Zeros (%)77.6%
Negative5064
Negative (%)21.7%
Memory size182.2 KiB
2025-03-09T15:52:07.798847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q10
median0
Q30
95-th percentile0
Maximum1401
Range1402
Interquartile range (IQR)0

Descriptive statistics

Standard deviation69.32887
Coefficient of variation (CV)13.859353
Kurtosis254.22496
Mean5.0023166
Median Absolute Deviation (MAD)0
Skewness15.223984
Sum116604
Variance4806.4922
MonotonicityNot monotonic
2025-03-09T15:52:07.910665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18078
77.6%
-1 5064
 
21.7%
1393 15
 
0.1%
709 10
 
< 0.1%
701 9
 
< 0.1%
352 7
 
< 0.1%
663 6
 
< 0.1%
91 5
 
< 0.1%
137 5
 
< 0.1%
708 5
 
< 0.1%
Other values (80) 106
 
0.5%
ValueCountFrequency (%)
-1 5064
 
21.7%
0 18078
77.6%
87 1
 
< 0.1%
91 5
 
< 0.1%
97 5
 
< 0.1%
132 1
 
< 0.1%
134 1
 
< 0.1%
137 5
 
< 0.1%
155 1
 
< 0.1%
163 1
 
< 0.1%
ValueCountFrequency (%)
1401 1
 
< 0.1%
1400 1
 
< 0.1%
1399 2
 
< 0.1%
1398 1
 
< 0.1%
1397 1
 
< 0.1%
1396 2
 
< 0.1%
1394 2
 
< 0.1%
1393 15
0.1%
1392 1
 
< 0.1%
1391 1
 
< 0.1%

PrixEtude
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
23310 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69930
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23310
100.0%

Length

2025-03-09T15:52:08.035173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T15:52:08.123235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23310
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46620
66.7%
Other Punctuation 23310
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46620
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46620
66.7%
. 23310
33.3%

Interactions

2025-03-09T15:51:41.847733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:30.570710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:34.926936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:36.973013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:39.022890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:41.800125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:45.869057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:50.244414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:54.589509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:59.369783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:03.953055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:07.804417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:12.353152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:16.193988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:20.413461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:24.792523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:29.109238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:32.786835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:36.913902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:39.917516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:41.952117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:30.772772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:35.025937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:37.068436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:39.131363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:42.139226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:46.065954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:50.447001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:54.797116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:59.560228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:04.185474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:08.013354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:12.525690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:16.436232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:20.630806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:24.993702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:29.283822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:32.974264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:37.119881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:40.071858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:42.048999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:30.980945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:35.180120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:37.190068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:39.272664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:42.660480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:46.261858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:50.643154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:55.097851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:59.792612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:04.333165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:08.201460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:12.709337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:16.657272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:20.835209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:25.235508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:29.496076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:33.175963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:37.322755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:40.175877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:42.141636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:31.179791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:35.258037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:37.270646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:39.374941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:42.898762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:46.435726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:50.807320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:55.539736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:59.967979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:04.485983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:08.662174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:12.889518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:16.855980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:21.283184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:25.445912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:29.706314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:33.350981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:37.516195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:40.260008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:42.230216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:31.370326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:35.350808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:37.397359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:39.463992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:43.104300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:46.601196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:51.029473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:55.780433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:00.268981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-09T15:51:11.025199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:15.106630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:19.150198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:23.569607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:27.802710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:31.623854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:35.769455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:39.298659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:41.366518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:43.374052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:33.715819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:36.513110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:38.490671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:40.858808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:44.922144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:49.138431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:53.517584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:58.270995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:02.801956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:06.899149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:11.305581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:15.282939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:19.383662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:23.781851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:28.007248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:31.854611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:35.942903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:39.392440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:41.459091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:43.452420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:33.927227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:36.600092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:38.606935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:40.961883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:45.142737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:49.348974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:53.761526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:58.492429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:03.000337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:07.093351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:11.509560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:15.458148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:19.579133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:23.946433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:28.209788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:32.009841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:36.124650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:39.481442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:41.538534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:43.532114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:34.362848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:36.683537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:38.734132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:41.180409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:45.318416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:49.563297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:53.940494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:58.672252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:03.241180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:07.279459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:11.759199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:15.654029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:19.798533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:24.134922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:28.384513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:32.223450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:36.325530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:39.615487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:41.622101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:43.608765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:34.564049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:36.772519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:38.837959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:41.405668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:45.541268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:49.777137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:54.154450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:58.919887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:03.471867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:07.452134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:11.934274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:15.839699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:20.004242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:24.328841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:28.591640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:32.418851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:36.502362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:39.704988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:41.691902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:43.692560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:34.729457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:36.860799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:38.928358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:41.567352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:45.705627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:49.966222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:54.367187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:50:59.131775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:03.713959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:07.615492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:12.128098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:15.990626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:20.178267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:24.569283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:28.880062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:32.572475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:36.677184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:39.789565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-09T15:51:41.774129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-09T15:52:08.275983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AppliqueFodecBoutonnageCodeDouaneEmballageEtatIDArFamilleIDArSousFamilleIDAr_CollectionIDAr_CouleurIDAr_LooksIDAr_ThemeIDArticleIDFibreCompositionIDFournisseurIDGrilleIDPatronnageIDPaysIDPlanComptableIDSaisonIDSupportArticleIDUsineIDcomplexiteMatiereModifieParNbrColisPaletteNbrPiecesColisNomenclatureValideNomenclatureValideParNumInternePieceCartonPoidsBrutPoidsEmballagePoidsNetPrixPrixAchatPrixFacPrixOutletReseauArtSaisiParSupportArtTauxTVATypeArticleService
AppliqueFodec1.0000.0000.0400.0000.1610.2680.2670.9030.4790.2610.3780.8760.0440.9460.2921.0000.6250.1120.8710.2890.8881.0001.0000.8880.0090.0200.0100.0130.8041.0000.0090.0610.0090.0130.0130.0130.0450.0000.9210.0000.6360.395
Boutonnage0.0001.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5000.0000.000
CodeDouane0.0400.0001.0000.0000.0000.5190.1620.0930.0000.0000.1140.0600.0000.1010.0510.0400.0180.0000.0300.0000.0450.0401.0000.0390.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0000.0000.0000.0000.1380.0000.0200.073
Emballage0.0000.0000.0001.0000.0000.0000.0000.0760.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0410.0000.0001.0000.1230.0000.0000.0000.0000.0000.0390.0000.0000.0000.0000.0000.0000.0000.0000.0690.0000.0130.000
Etat0.1610.0000.0000.0001.0000.0350.0340.1510.0800.0480.0820.1260.0600.1060.0400.1610.0510.0000.0890.0600.1760.1610.0000.5930.0000.0000.0000.0000.0840.1120.0000.0140.0000.0000.0000.0000.0310.0000.6730.0000.1280.015
IDArFamille0.2680.0000.5190.0000.0351.0000.215-0.1340.074-0.025-0.037-0.0490.020-0.1180.3960.2680.2370.1230.059-0.018-0.1020.2680.7340.2450.0000.0230.0000.000-0.1500.165-0.0050.020-0.014-0.102-0.158-0.1790.0110.0000.2920.0000.1330.093
IDArSousFamille0.2670.0100.1620.0000.0340.2151.0000.066-0.0180.0460.0750.0600.0420.0790.2890.267-0.131-0.0660.0490.0490.0550.2670.6820.1950.0000.0000.0000.000-0.0160.1730.0170.0240.1050.0450.0380.0250.0130.0000.2190.0100.1120.133
IDAr_Collection0.9030.0000.0930.0760.151-0.1340.0661.000-0.6980.3060.4220.7300.0680.927-0.2100.9030.323-0.787-0.4730.3430.8350.9030.7690.4890.0000.0490.0140.0100.8590.5640.0750.799-0.588-0.476-0.210-0.2240.1150.0090.6130.0000.4910.525
IDAr_Couleur0.4790.0000.0000.0000.0800.074-0.018-0.6981.000-0.222-0.303-0.5260.000-0.6920.1980.479-0.2920.5520.489-0.238-0.6180.4791.0000.1510.0000.0000.0000.000-0.6930.277-0.0660.0260.4710.4140.2050.2550.0270.0000.1530.0000.1770.143
IDAr_Looks0.2610.0000.0000.0000.048-0.0250.0460.306-0.2221.0000.4010.2540.0000.308-0.0240.2610.034-0.340-0.1080.5560.2740.2610.6260.2410.0000.0000.0000.0000.2250.272-0.0060.000-0.206-0.230-0.056-0.1120.0000.0000.3590.0000.4940.256
IDAr_Theme0.3780.0000.1140.0000.082-0.0370.0750.422-0.3030.4011.0000.3430.0000.364-0.0520.3780.008-0.412-0.1020.4300.2370.3780.8180.2740.0000.5770.0000.0000.2810.245-0.0130.000-0.257-0.314-0.201-0.2680.0160.0000.3710.0000.3530.426
IDArticle0.8760.0030.0600.0030.126-0.0490.0600.730-0.5260.2540.3431.0000.0580.713-0.1560.8760.150-0.657-0.3700.2650.6380.8760.8750.3960.0050.0210.0290.0150.6730.5120.0460.137-0.555-0.528-0.272-0.2390.0390.0030.4700.0030.4400.519
IDFibreComposition0.0440.0000.0000.0000.0600.0200.0420.0680.0000.0000.0000.0581.0000.0190.0080.0440.0200.0000.0410.0700.0300.0441.0000.2750.0000.0000.0000.0000.0520.0320.0000.0000.0000.0000.0000.0000.0000.0000.1440.0000.0000.019
IDFournisseur0.9460.0000.1010.0000.106-0.1180.0790.927-0.6920.3080.3640.7130.0191.000-0.1720.9460.335-0.743-0.5050.3200.9020.9460.7770.4700.0040.0000.0000.0000.8800.7050.0880.080-0.585-0.445-0.189-0.2170.0210.0000.4880.0000.4060.396
IDGrille0.2920.0000.0510.0000.0400.3960.289-0.2100.198-0.024-0.052-0.1560.008-0.1721.0000.2920.2000.1320.198-0.044-0.1660.2920.8820.1360.0000.0000.0000.000-0.2330.189-0.0030.026-0.022-0.136-0.355-0.3660.0280.0000.1340.0000.1210.065
IDPatronnage1.0000.0000.0400.0000.1610.2680.2670.9030.4790.2610.3780.8760.0440.9460.2921.0000.6250.1120.8710.2890.8881.0001.0000.8880.0090.0200.0100.0130.8041.0000.0090.0610.0090.0130.0130.0130.0450.0000.9210.0000.6360.395
IDPays0.6250.0000.0180.0000.0510.237-0.1310.323-0.2920.0340.0080.1500.0200.3350.2000.6251.000-0.253-0.1550.0040.4340.6250.6490.2210.0000.0060.0000.0000.2960.363-0.0620.068-0.302-0.239-0.168-0.1970.0130.0000.2500.0000.2310.139
IDPlanComptable0.1120.0000.0000.0000.0000.123-0.066-0.7870.552-0.340-0.412-0.6570.000-0.7430.1320.112-0.2531.0000.316-0.340-0.6530.1121.0000.3190.0000.0000.0000.000-0.6830.0680.0120.0000.5020.4790.1490.1580.0000.0000.3110.0000.0190.000
IDSaison0.8710.0000.0300.0000.0890.0590.049-0.4730.489-0.108-0.102-0.3700.041-0.5050.1980.871-0.1550.3161.000-0.117-0.4970.8711.0000.3810.0000.0000.0000.000-0.6020.5070.0590.7460.4610.2580.1570.1560.0650.0000.4330.0000.3240.340
IDSupportArticle0.2890.0000.0000.0410.060-0.0180.0490.343-0.2380.5560.4300.2650.0700.320-0.0440.2890.004-0.340-0.1171.0000.2490.2890.7660.2580.0000.0200.0000.0000.2320.2470.0130.000-0.225-0.216-0.100-0.1420.0150.0000.4740.0000.4390.156
IDUsine0.8880.0000.0450.0000.176-0.1020.0550.835-0.6180.2740.2370.6380.0300.902-0.1660.8880.434-0.653-0.4970.2491.0000.8880.6910.5110.0260.0140.0200.0130.8400.6550.0960.333-0.528-0.373-0.120-0.1480.0470.0000.5630.0000.4630.414
IDcomplexite1.0000.0000.0400.0000.1610.2680.2670.9030.4790.2610.3780.8760.0440.9460.2921.0000.6250.1120.8710.2890.8881.0001.0000.8880.0090.0200.0100.0130.8041.0000.0090.0610.0090.0130.0130.0130.0450.0000.9210.0000.6360.395
Matiere1.0001.0001.0001.0000.0000.7340.6820.7691.0000.6260.8180.8751.0000.7770.8821.0000.6491.0001.0000.7660.6911.0001.0000.4561.0001.0001.0001.0000.6110.5571.0001.0001.0001.0001.0001.0001.0001.0000.4881.0000.8380.357
ModifiePar0.8880.0000.0390.1230.5930.2450.1950.4890.1510.2410.2740.3960.2750.4700.1360.8880.2210.3190.3810.2580.5110.8880.4561.0000.0330.0230.0130.0170.3220.6380.0960.6310.0160.0000.0000.0000.1310.0220.4730.0000.5460.552
NbrColisPalette0.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0040.0000.0090.0000.0000.0000.0000.0260.0091.0000.0331.0000.7070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1280.0000.0050.014
NbrPiecesColis0.0200.0000.0000.0000.0000.0230.0000.0490.0000.0000.5770.0210.0000.0000.0000.0200.0060.0000.0000.0200.0140.0201.0000.0230.7071.0000.0000.0000.0000.0510.0000.0000.0000.0000.0000.0000.0000.0000.1470.0000.0090.032
NomenclatureValide0.0100.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0290.0000.0000.0000.0100.0000.0000.0000.0000.0200.0101.0000.0130.0000.0001.0001.0000.0160.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.000
NomenclatureValidePar0.0130.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0150.0000.0000.0000.0130.0000.0000.0000.0000.0130.0131.0000.0170.0000.0001.0001.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.000
NumInterne0.8040.0000.0000.0000.084-0.150-0.0160.859-0.6930.2250.2810.6730.0520.880-0.2330.8040.296-0.683-0.6020.2320.8400.8040.6110.3220.0000.0000.0160.0001.0000.4720.0450.010-0.611-0.457-0.213-0.2050.0160.0000.3690.0000.3250.345
PieceCarton1.0000.0000.0290.0390.1120.1650.1730.5640.2770.2720.2450.5120.0320.7050.1891.0000.3630.0680.5070.2470.6551.0000.5570.6380.0000.0510.0130.0040.4721.0000.0610.0660.0000.0040.0040.0040.0750.0000.7150.0000.3740.342
PoidsBrut0.0090.0000.0000.0000.000-0.0050.0170.075-0.066-0.006-0.0130.0460.0000.088-0.0030.009-0.0620.0120.0590.0130.0960.0091.0000.0960.0000.0000.0000.0000.0450.0611.0000.0000.061-0.044-0.059-0.0520.0000.0000.1000.0000.0130.000
PoidsEmballage0.0610.0000.0000.0000.0140.0200.0240.7990.0260.0000.0000.1370.0000.0800.0260.0610.0680.0000.7460.0000.3330.0611.0000.6310.0000.0000.0000.0000.0100.0660.0001.0000.0000.0000.0000.0000.1310.0000.8300.0000.1180.008
PoidsNet0.0090.0000.0000.0000.000-0.0140.105-0.5880.471-0.206-0.257-0.5550.000-0.585-0.0220.009-0.3020.5020.461-0.225-0.5280.0091.0000.0160.0000.0000.0000.000-0.6110.0000.0610.0001.0000.5440.4560.4980.0000.0000.0000.0000.0130.000
Prix0.0130.0000.0000.0000.000-0.1020.045-0.4760.414-0.230-0.314-0.5280.000-0.445-0.1360.013-0.2390.4790.258-0.216-0.3730.0131.0000.0000.0000.0000.0000.000-0.4570.004-0.0440.0000.5441.0000.6620.5980.0000.0000.0000.0000.0350.000
PrixAchat0.0130.0000.0000.0000.000-0.1580.038-0.2100.205-0.056-0.201-0.2720.000-0.189-0.3550.013-0.1680.1490.157-0.100-0.1200.0131.0000.0000.0000.0000.0000.000-0.2130.004-0.0590.0000.4560.6621.0000.8130.0000.0000.0000.0000.0350.000
PrixFac0.0130.0000.0000.0000.000-0.1790.025-0.2240.255-0.112-0.268-0.2390.000-0.217-0.3660.013-0.1970.1580.156-0.142-0.1480.0131.0000.0000.0000.0000.0000.000-0.2050.004-0.0520.0000.4980.5980.8131.0000.0000.0000.0000.0000.0350.000
PrixOutlet0.0450.0000.0000.0000.0310.0110.0130.1150.0270.0000.0160.0390.0000.0210.0280.0450.0130.0000.0650.0150.0470.0451.0000.1310.0000.0000.0000.0000.0160.0750.0000.1310.0000.0000.0000.0001.0000.0000.1270.0000.0260.000
ReseauArt0.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0060.0000.0130.022
SaisiPar0.9210.0000.1380.0690.6730.2920.2190.6130.1530.3590.3710.4700.1440.4880.1340.9210.2500.3110.4330.4740.5630.9210.4880.4730.1280.1470.0000.0000.3690.7150.1000.8300.0000.0000.0000.0000.1270.0061.0000.0000.6320.638
SupportArt0.0000.5000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
TauxTVA0.6360.0000.0200.0130.1280.1330.1120.4910.1770.4940.3530.4400.0000.4060.1210.6360.2310.0190.3240.4390.4630.6360.8380.5460.0050.0090.0230.0170.3250.3740.0130.1180.0130.0350.0350.0350.0260.0130.6320.0001.0000.104
TypeArticleService0.3950.0000.0730.0000.0150.0930.1330.5250.1430.2560.4260.5190.0190.3960.0650.3950.1390.0000.3400.1560.4140.3950.3570.5520.0140.0320.0000.0000.3450.3420.0000.0080.0000.0000.0000.0000.0000.0220.6380.0000.1041.000

Missing values

2025-03-09T15:51:44.054922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-09T15:51:44.820277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-09T15:51:45.469823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

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002BHNP282CITY P1 BLACK035superviseur2023-11-02superviseur2024-09-181000.018.5000.0None00.096% POLYESTER 4% ELASTANE WOVEN\r\n0.00.000.0000.00.00.00.00.003110142624_24K04145-1000000028900None000.00010.00.000.00.00.00.000.00None0000000None0.00.0None000000.00.0-10.00018.5002551710.00.0002024-10-04180000.00None00.0None00.000.0
105Report à NouveauReport à Nouveau002023-11-02None2000.00.0000.0None00.00.00.000.0000.00.00.00.00.000000014500000000000None000.00000.00.000.00.00.00.000.00None0000000None0.00.0None000000.00.000.0000.000000.00.000None0000.00None00.0None00.000.0
226AENC88LOUISON C1 LOUISON NOIR1192superviseur2023-11-02superviseur2023-11-201000.07.182-1.0None00.0TP:100% PES0.049.990.0000.00.00.00.00.0019101LOUISON C101145-1000000021300None000.00010.00.000.00.00.00.000.00None0000000None0.00.0None000000.00.0-10.0007.1822551720.00.0-10None97000.00None00.0None00.000.0
307AENU104ANATALIA ECRU LUREX LIGHT GOLD1204superviseur2023-11-02superviseur2023-10-311000.013.5000.0None00.066%Acrylique 18%Polyamide 8%Laine0.049.990.0000.00.00.00.00.00-102NATALIA00145-10000000000None000.00010.00.000.00.00.00.000.00None0000000None0.00.0None000000.00.0-10.00013.500255180.00.0-10None0000.00None00.0None00.000.0
408AENU102AANANNI GRIS CHAINE SILVER LUREX-20021294superviseur2023-11-02superviseur2023-11-061000.013.7000.0None00.040% ACR 30% PA 30% MOHAIR0.054.990.0000.00.00.00.00.00-102AENU102A00145-10000000000None000.00010.00.000.00.00.00.000.00None0000000None0.00.0None000000.00.0-10.00013.700255-10.00.0-10None0000.00None00.0None00.000.0
509AENU102NANNI ROUGE POMPIER LUREX-20451254superviseur2023-11-02superviseur2023-11-061000.013.7000.0None00.040% ACR 30% PA 30% MOHAIR0.054.990.0000.00.00.00.00.00-102AENU10200145-10000000000None000.00010.00.000.00.00.00.000.00None0000000None0.00.0None000000.00.0-10.00013.700255-10.00.0-10None0000.00None00.0None00.000.0
6010AENU105ANICOLO CREME-71911314superviseur2023-11-02superviseur2023-11-021000.015.7100.0None00.050%NY 30%ACR 20%BABY ALPAGA0.054.990.0000.00.00.00.00.00-102AENU10500145-10000000000None000.00010.00.000.00.00.00.000.00None0000000None0.00.0None000000.00.0-10.00015.710255180.00.0-10None0000.00None00.0None00.000.0
7011AENU105NICOLO VERT SAPIN1304superviseur2023-11-02superviseur2023-11-021000.015.7100.0None00.050%NY 30%ACR 20%BABY ALPAGA0.054.990.0000.00.00.00.00.00-102AENU105A00145-10000000000None000.00010.00.000.00.00.00.000.00None0000000None0.00.0None000000.00.0-10.00015.710255180.00.0-10None0000.00None00.0None00.000.0
8012AENU119ANORMA BLEU LUCCIO-21881444superviseur2023-11-02s.hayriye2024-12-171000.015.7100.0None00.050%NY 30%ACR 20%BABY ALPAGA0.054.990.0000.00.00.00.00.00-102AENU119A00145-10000000000None000.00010.00.000.00.00.00.000.00None0000000None0.00.0None000000.00.0-10.00012.900255180.00.0-10None0000.00None00.0None00.0-10.0
9013AENU119NORMA NOIR-20591224superviseur2023-11-02superviseur2023-11-021000.012.8000.0None00.050%NY 30%ACR 20%BABY ALPAGA0.054.990.0000.00.00.00.00.00-102AENU11900145-10000000000None000.00010.00.000.00.00.00.000.00None0000000None0.00.0None000000.00.0-10.00012.800255180.00.0-10None0000.00None00.0None00.000.0
IDAr_CollectionIDArticleCodeArticleIDAr_CouleurIDArFamilleSaisiParSaisiLeModifieParModifieLeEtatIDGammeIDClientTempsClientPrixTauxTVAImageIdProcessprixMPCodeDouaneCompositionValeurPrixFacCadenceIdArticleBaseSemiFiniPoidsBrutPoidsNetValeurTissuValeurFournitureValeurMPTypeTarifIDArSousFamilleIdMeilleurOFIDGrilleReferenceNomenclatureValideIDSaisonNumInterneBaseStylismeIDPatronnageIDTypeMatiereBaseIDVarianteModeleIDGenreIDBroderieIDSerigraphieIDGarnitureIDTypeAccessoireIDFournisseurIDTransfertIDCouleurGarnitureObservationsIDCouleurBroderieIDCouleurSerigraphiePrixEmballageStockMinStockAlerteAppliqueFodecValeurMPEuroValeurMPAutreTypeArticleServiceValeurMPTunisieValeurMPEuromedAQLAQLMineurIDNiveauControleAQLCritiqueIDCategorieDateValidationNomenclatureNomenclatureValideParIDCategoriereclamationIDCartoucheIDArticleParentisParentQteFilsNbrPiecesColisNbrColisPaletteDimensionsTempsAtelierTempsFinitionsArticleLongIDTypeMatelassageIDMPIsMPIDDecorArticleIsSemiFiniPoidsEmballageTempsUnitaireIDcomplexiteTauxSondageQlteIDNormeIDAr_ThemePrixAchatEmballageBoutonnagePieceCartonSupportArtIDPaysReseauArtReferenceFssrDDVFraisTransportAutresFraisIDFibreCompositionIDArticleEtqEntretienDateMEPIDUsineIDSupportArticleEcologiqueTauxDefectueuxPublierCompositionMatiereIDAr_LooksPrixOutletMatiereOrdreTauxCommissionCACODE_OLDIDPlanComptablePrixEtude
233001323874DHNXA023SSBEVERLY GANT025UP.Ceren2025-02-26UP.Ceren2025-02-271000.00.00.2None00.042%Acrylic,27%Nylon,31%Poly0.00.00.0000.00.00.00.00.0027003DHNXA0230625-1000000028900SMS000.00010.00.02550.00.00.00.000.00None0000000None0.00.0BEVERLY GANT CORAIL000000.00.0-10.0001.872551730.00.0002025-03-05206500.00None00.0None00.0-10.0
233011323875DHNXA023SSBEVERLY GANT025UP.Ceren2025-02-26UP.Ceren2025-02-271000.00.00.2None00.042%Acrylic,27%Nylon,31%Poly0.00.00.0000.00.00.00.00.0027003DHNXA0230625-1000000028900SMS000.00010.00.02550.00.00.00.000.00None0000000None0.00.0BEVERLY GANT KAKI000000.00.0-10.0001.872551730.00.0002025-03-05206500.00None00.0None00.0-10.0
233021323876DHNXA023SSBEVERLY GANT025UP.Ceren2025-02-26UP.Ceren2025-02-271000.00.00.2None00.042%Acrylic,27%Nylon,31%Poly0.00.00.0000.00.00.00.00.0027003DHNXA0230625-1000000028900SMS000.00010.00.02550.00.00.00.000.00None0000000None0.00.0BEVERLY GANT BLUE DENIM000000.00.0-10.0001.872551730.00.0002025-03-05206500.00None00.0None00.0-10.0
233031123877CENXA018KIT DELOVA MULTICOLORE025UP.Ceren2025-02-27UP.Ceren2025-02-271000.00.00.0None00.0100%PES0.010.00.0000.00.00.00.00.0030903CENXA0180518-1000000021300None000.00010.00.000.00.00.00.000.00None0000000None0.00.0KIT DELOVA MULTICOLORE000000.00.0-10.0002.252551720.00.0002025-03-2897000.00None00.0None00.0-10.0
233041123878CENXA019KIT JORDAN MULTICOLORE025UP.Ceren2025-02-27UP.Ceren2025-02-271000.00.00.0None00.0100%CV0.010.00.0000.00.00.00.00.0030903CENXA0190519-1000000021300None000.00010.00.000.00.00.00.000.00None0000000None0.00.0KIT JORDAN MULTICOLORE000000.00.0-10.0002.252551720.00.0002025-03-1497000.00None00.0None00.0-10.0
233051123879CENXA020KIT KYOTO MULTICOLORE025UP.Ceren2025-02-27UP.Ceren2025-02-271000.00.00.0None00.0100%CV0.010.00.0000.00.00.00.00.0030903CENXA0200520-1000000021300None000.00010.00.000.00.00.00.000.00None0000000None0.00.0KIT KYOTO MULTICOLORE000000.00.0-10.0002.252551720.00.0002025-03-1497000.00None00.0None00.0-10.0
233061123880CENXA021CHOUCHOU TOLOSANE NOIR/BLANC025UP.Ceren2025-02-272025-02-271000.00.00.0None00.0100%PES0.05.00.0000.00.00.00.00.0030903CENXA0210521-1000000021300None000.00010.00.000.00.00.00.000.00None0000000None0.00.0CHOUCHOU TOLOSANE NOIR/BLANC000000.00.0-10.0001.252551720.00.0002025-03-1497000.00None00.0None00.0-10.0
233071123881CENB148FLORIAN SH101UP.Ceren2025-02-27None1000.00.00.0None00.00.00.00.0000.00.00.00.00.0019001CENB14805148-1000000021300None000.00010.00.000.00.00.00.000.00None0000000None0.00.0FLORIAN SH1 LIN RAYÉ LUREX000000.00.0-10.00600.002551720.00.000None97000.00None00.0None00.0-10.0
233081123882CENT227PENELOPE T1 MIDNIGHT BLUE06b.zuhal2025-02-28b.zuhal2025-02-281000.00.00.0None00.0100% COTON0.029.00.0000.00.00.00.00.0021602CENT22705227-1000000028900None000.00010.00.000.00.00.00.000.00None0000000None0.00.0PENELOPE T1 MIDNIGHT BLUE000000.00.0-10.0006.002551710.00.0002025-03-15172000.00None00.0None00.0-10.0
233091123883CENT227PENELOPE T1 BLANC06b.zuhal2025-02-28b.zuhal2025-02-281000.00.00.0None00.0100% COTON0.029.00.0000.00.00.00.00.0021602CENT22705228-1000000028900None000.00010.00.000.00.00.00.000.00None0000000None0.00.0PENELOPE T1 BLANC000000.00.0-10.0006.002551710.00.0002025-03-15172000.00None00.0None00.0-10.0